text to speech human voice free online

Voice   Generator

This web app allows you to generate voice audio from text - no login needed, and it's completely free! It uses your browser's built-in voice synthesis technology, and so the voices will differ depending on the browser that you're using. You can download the audio as a file, but note that the downloaded voices may be different to your browser's voices because they are downloaded from an external text-to-speech server. If you don't like the externally-downloaded voice, you can use a recording app on your device to record the "system" or "internal" sound while you're playing the generated voice audio.

Want more voices? You can download the generated audio and then use voicechanger.io to add effects to the voice. For example, you can make the voice sound more robotic, or like a giant ogre, or an evil demon. You can even use it to reverse the generated audio, randomly distort the speed of the voice throughout the audio, add a scary ghost effect, or add an "anonymous hacker" effect to it.

Note: If the list of available text-to-speech voices is small, or all the voices sound the same, then you may need to install text-to-speech voices on your device. Many operating systems (including some versions of Android, for example) only come with one voice by default, and the others need to be downloaded in your device's settings. If you don't know how to install more voices, and you can't find a tutorial online, you can try downloading the audio with the download button instead. As mentioned above, the downloaded audio uses external voices which may be different to your device's local ones.

You're free to use the generated voices for any purpose - no attribution needed. You could use this website as a free voice over generator for narrating your videos in cases where don't want to use your real voice. You can also adjust the pitch of the voice to make it sound younger/older, and you can even adjust the rate/speed of the generated speech, so you can create a fast-talking high-pitched chipmunk voice if you want to.

Note: If you have offline-compatible voices installed on your device (check your system Text-To-Speech settings), then this web app works offline! Find the "add to homescreen" or "install" button in your browser to add a shortcut to this app in your home screen. And note that if you don't have an internet connection, or if for some reason the voice audio download isn't working for you, you can also use a recording app that records your devices "internal" or "system" sound.

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If you like this project check out these: AI Chat , AI Anime Generator , AI Image Generator , and AI Story Generator .

Realistic Text-to-Speech AI converter

Create realistic Voiceovers online! Insert any text to generate speech and download audio mp3 or wav for any purpose. Speak a text with AI-powered voices.You can convert text to voice for free for reference only. For all features, purchase the paid plans

How to convert text into speech?

• Just type some text or import your written content
• Press "generate" button
• Download MP3 / WAV

Full list of benefits of neural voices

Multi-voice editor.

Dialogue with AI Voices . You can use several voices at once in one text.

Over 1000 Natural Sounding Voices

Crystal-clear voice over like a Human. Males, females, children's, elderly voices.

You spend little on re-dubbing the text. Limits are spent only for changed sentences in the text. Read more about our cost-effective Limit System . Enjoy full control over your spending with one-time payments for only what you use. Pay as you go : get flexible, cost-effective access to our neural network voiceover services without subscriptions.

If your Limit balance is sufficient, you can use a single query to convert a text of up to 2,000,000 characters into speech.

Commercial Use

You can use the generated audio for commercial purposes. Examples: YouTube, Tik Tok, Instagram, Facebook, Twitch, Twitter, Podcasts, Video Ads, Advertising, E-book, Presentation and other.

Custom voice settings

Change Speed, Pitch, Stress, Pronunciation, Intonation , Emphasis , Pauses and more. SSML support .

SRT to audio

Subtitles to Audio : Convert your subtitle file into perfectly timed multilingual voiceovers with our advanced neural networks.

Downloadable TTS

You can download converted audio files in MP3, WAV, OGG for free.

Powerful support

We will help you with any questions about text-to-speech. Ask any questions, even the simplest ones. We are happy to help.

Compatible with editing programs

Works with any video creation software: Adobe Premier, After effects, Audition, DaVinci Resolve, Apple Motion, Camtasia, iMovie, Audacity, etc.

Cloud save your history

All your files and texts are automatically saved in your profile on our cloud server. Add tracks to your favorites in one click.

Use our text to voice converter to make videos with natural sounding speech!

Say goodbye to expensive traditional audio creation

Cheap price. Create a professional voiceover in real time for pennies. it is 100 times cheaper than a live speaker.

Traditional audio creation

• Expensive live speakers, high prices
• A long search for freelancers and studios
• Editing requires complex tools and knowledge
• The announcer in the studio voices a long time. It takes time to give him a task and accept it.

• Affordable tts generation starting at $0.08 per 1000 characters
• Website accessible in your browser right now
• Intuitive interface, suitable for beginners
• SpeechGen generates text from speech very quickly. A few clicks and the audio is ready.

Create AI-generated realistic voice-overs.

Ways to use. Cases.

See how other people are already using our realistic speech synthesis. There are hundreds of variations in applications. Here are some of them.

• Voice over for videos. Commercial, YouTube, Tik Tok, Instagram, Facebook, and other social media. Add voice to any videos!
• E-learning material. Ex: learning foreign languages, listening to lectures, instructional videos.
• Advertising. Increase installations and sales! Create AI-generated realistic voice-overs for video ads, promo, and creatives.
• Public places. Synthesizing speech from text is needed for airports, bus stations, parks, supermarkets, stadiums, and other public areas.
• Podcasts. Turn text into podcasts to increase content reach. Publish your audio files on iTunes, Spotify, and other podcast services.
• Mobile apps and desktop software. The synthesized ai voices make the app friendly.
• Essay reader. Read your essay out loud to write a better paper.
• Presentations. Use text-to-speech for impressive PowerPoint presentations and slideshow.
• Reading documents. Save your time reading documents aloud with a speech synthesizer.
• Book reader. Use our text-to-speech web app for ebook reading aloud with natural voices.
• Welcome audio messages for websites. It is a perfect way to re-engage with your audience. 
• Online article reader. Internet users translate texts of interesting articles into audio and listen to them to save time.
• Voicemail greeting generator. Record voice-over for telephone systems phone greetings.
• Online narrator to read fairy tales aloud to children.
• For fun. Use the robot voiceover to create memes, creativity, and gags.

Maximize your content’s potential with an audio-version. Increase audience engagement and drive business growth.

Who uses Text to Speech?

SpeechGen.io is a service with artificial intelligence used by about 1,000 people daily for different purposes. Here are examples.

Video makers create voiceovers for videos. They generate audio content without expensive studio production.

Newsmakers convert text to speech with computerized voices for news reporting and sports announcing.

Students and busy professionals to quickly explore content

Foreigners. Second-language students who want to improve their pronunciation or listen to the text comprehension

Software developers add synthesized speech to programs to improve the user experience.

Marketers. Easy-to-produce audio content for any startups

IVR voice recordings. Generate prompts for interactive voice response systems.

Educators. Foreign language teachers generate voice from the text for audio examples.

Booklovers use Speechgen as an out loud book reader. The TTS voiceover is downloadable. Listen on any device.

HR departments and e-learning professionals can make learning modules and employee training with ai text to speech online software.

Webmasters convert articles to audio with lifelike robotic voices. TTS audio increases the time on the webpage and the depth of views.

Animators use ai voices for dialogue and character speech.

Text to Speech enables brands, companies, and organizations to deliver enhanced end-user experience, while minimizing costs.

Frequently Asked Questions

Convert any text to super realistic human voices. See all tariff plans .

Enhance Your Content Accessibility

Boost your experience with our additional features. Easily convert PDFs, DOCx files, and video subtitles into natural-sounding audio.

📄🔊 PDF to Audio

Transform your PDF documents into audible content for easier consumption and enhanced accessibility.

📝🎧 DOCx to mp3

Easily convert Word documents into speech for listening on the go or for those who prefer audio format

🔊📰 WordPress plugin

Enhance your WordPress site with our plugin for article voiceovers, embedding an audio player directly on your site to boost user engagement and diversify your content.

Supported languages

• Amharic (Ethiopia)
• Arabic (Algeria)
• Arabic (Egypt)
• Arabic (Saudi Arabia)
• Bengali (India)
• Catalan (Spain)
• English (Australia)
• English (Canada)
• English (GB)
• English (Hong Kong)
• English (India)
• English (Philippines)
• German (Austria)
• Hindi India
• Spanish (Argentina)
• Spanish (Mexico)
• Spanish (United States)
• Tamil (India)
• All languages: +76

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Text to Speech Voice Over with Realistic AI Voices

Murf offers a selection of 100% natural sounding AI voices in 20+ languages to make professional voice over for your videos and presentations. Start your free trial.

Quality Guaranteed, No Robotic Voices

Our voices are all human sounding and quality checked across dozens of parameters. Gone are the days of robotic text to speech, most people can’t even tell between our advanced AI voices and recorded human voices.

Text to Speech Voices in 20+ Languages

Murf offers a selection of voices across 20+ languages. Most languages have voices available for testing quality in the free plan. Some languages also support multiple accents like English, Spanish and Portuguese.

A Simple Text to Voice Converter

High-Quality Voices for Every Use Case

Not Just a Text to Speech Tool

Emphasize specific words

Want to make your voiceover sound interesting? Use Murf’s ‘Emphasis’ feature to put that extra force on syllables, words, or phrases that add life to your voiceover.

Take control of your narration with pitch

Use Murf’s ‘Pitch’ functionality to draw the listeners' attention to words or phrases expressing emotions. Customize the voice as you like to make it work for yourself.

Elevate your story with pauses

Add pauses of varying lengths to your narration using Murf’s ‘Pause’ feature to give the listener's attention powers a rest and prepare them to receive your message.

Perfect Word Pronunciation

Articulate words accurately and enhance clarity in speech by customizing pronunciation. Use alternative spellings or IPAs to achieve the right pronunciation.

Fine Tune Narration Speed

Effortlessly increase or decrease the pace of the voiceover to ensure it aligns with the rhythm and flow of the message.

Expressive Voice Style Palette

Infuse your narration with the exact emotion your content needs using Murf’s dynamic voice styles. Choose from versatile options like excited, sad, angry, calm, terrified, friendly, and more.

Text to Voice Generator Made Easy

Reliable and secure. your data, our promise..

Why Use Murf AI Text to Speech?

Murf's text to audio software changes the way you create and edit voiceovers with lifelike, flawless AI voices. What used to take hours, weeks, or even months now only takes minutes. You can also include images, videos, and presentations to your voiceover and sync them together without the need for a third-party tool. Here are a few reasons why you should use Murf's text to speech.

Save time and hundreds of dollars in recording expensive voice overs.

Editing voice over is as simple as editing text. Just cut, copy paste and render.

Create a consistent brand voice across all your customer touchpoints.

Connect with global customers effectively with our multiple language AI voices.

Build scalable voice applications with Murf’s Text to Speech API.

Tts voice over in 20+ languages.

@MURFAISTUDIO

Hear from Our Customers

Murf allows me to create TTS voiceovers in a matter of minutes. Previously, I had a tedious process of sending scripts out to agencies and waited days to get voiceovers back. With Murf, I can make changes whenever I like, diversify my speaker portfolio by picking new voices instantly, and even ramp up my course localization.

Murf it's an amazing text-to-speech AI voice generator, easy to work with, flexible and reliable. Its voices, non-pro and pro (either English, Spanish, and French), are both so real that many clients of mine have been surprised to know that they were not from professional voice-over actors.

I recently tried murf.ai and I have to say I am thoroughly impressed. The quality of the generated voice is exceptional and very realistic, which is important for my business needs. The platform is user-friendly and easy to navigate, and the range of voices available is impressive.

This website is so easy and clear that you will find yourself mastering all the tools in no time. The fact that regenerating the voice with different voices, punctuations, and tones does not deduct from your allowed minutes is so fair and reasonable. And the price is affordable too. Highly recommended

This is the most human-like voice I was able to find. It's very lively,and I found it suitable for many types of videos including marketing and e-learning, it kept my audience engaged!

I just started to create a video channel about historical figures, and Murf.ai really brings them to life. I found my top voice for my scripts, and the easy integration of video elements makes it a breeze to create informative videos. I also like the easy changes one can make to the tone of voice from within the editor.

Frequently Asked Questions

What is text to speech.

Text to speech is the generation of synthesized speech from text using AI. It was primarily designed as an assistive technology to help individuals with hearing impairments, visual and learning disabilities, and aged citizens to understand and consume content in a better manner. Today, the applications of text to voice have grown manifold, and range from content creation to voiceover generation to customer service, and more. With a touch of a button, text to speech converter can take words on a computer or other digital device and convert them into audio files. Today, the technology is used to create narratives for explainer videos or product demos , turn a book into an audio book, generate voiceovers for elearning materials, training videos, ads and commercials, YouTube videos, or podcasts, among other things.

How does text to speech converter work?

Text to speech online software leverages AI and deep learning algorithms to process the written input and sythesize a spoken output. The written text is first broken down into individual words and phrases by the text to speech AI software’s text analysis component and then various rules and algorithms are applied to determine the appropriate pronunciation, inflection, and emphasis for each word. The speech synthesis component of the software then takes this information along with pre-recorded sound samples of individual phonemes and uses it to generate the spoken words and sentences, which is then spoken out loud using a synthesized voice generated by a computer or other device. 

Top Five Use Cases of Text to Speech Online Software

From increasing brand visibility and customer traction to improving customer service and boosting customer engagement to helping people with visual impairments, reading difficulties, and learning disabilities, text to voice generator is proving to be a game-changing technology across industries. 

Considering the myriad of benefits offered by TTS technology and how simple they make information retention, businesses are integrating AI text to speech into their workflow in one form or another. Here is a glimpse of all the ways text to speech tool is currently being utilized:

TTS in Assistive Technology 

For quite some time now, text to speech apps and software has been used as an accessibility tool for individuals with a variety of special needs linked to Dyslexia, visual impairments, or other disabilities that make it difficult to read traditional text. Using TTS readers, people facing such problems can convert text to speech and learn by listening on the go. Text to speech converters also improve literacy and comprehension skills. When used in language education, they can make learning more engaging. For example, it's much easier and faster to apprehend a foreign language when listening to the live translation of written words with correct intonation and pronunciation than when reading. 

TTS in Translations

Given the fact that modern text to speech solutions come with multilingual support, brands can reach local customers by converting their content from text to audio in the local language. This will help target and connect with native-speaking customers or audiences in remote areas. 

Furthermore, text to speech online solutions can also be used to translate content from one language to another. This is especially beneficial for users who come across a piece of content in a language they don't understand and can have it read aloud in their native language or a language they are adept at for better understanding.

TTS in Customer Service

With advancements in speech synthesis, it has become easier to create text and convert it to pre-recorded voices for interactive voice response calls. Today's voice text to speech technology comes with human-like AI voices that can make natural human conversations on IVR calls. This helps contact centers provide personalized customer interactions without requiring assistance from live agents. 

TTS serves as both an inbound and outbound customer service tool. For example, when used in tandem with an IVR system, text to voice generators can provide personalized information to callers, such as greeting a customer by name, providing account information, confirming details about the order, payment, or appointment, and more. Furthermore, by tapping into the extensive range of languages, accents, and a wide variety female and male voices offered by TTS online software, companies can provide an experience that matches their customer's profiles or help promote an image for their brand. 

TTS in Automotive Industry

Text to audio solutions help make connected and autonomous cars safer and sound truly unique, begetting an on-road revolution. They can be used in in-car conversational systems for navigational prompts and map data, infotainment systems to read aloud information about the car, such as fuel level or tire pressure, and swap music and voice assistants to place phone calls, read messages, and more.

TTS in Healthcare

In the healthcare industry, text to speech voices can be used to read aloud patient information, instructions for taking medication, and provide information to doctors and other medical professionals about upcoming appointments, scheduling calls, and more. 

Why text to speech matters for businesses?

It's an exciting time to stake your claim in the realm of speech synthesis. There are a number of key industries where the text to speech technology has already succeeded in making a dent. Here are a few different ways in which businesses can harness the power of text-to- speech and save money and time:

Enhances customer experience

Any business can leverage text to voice generators to alleviate human agent workload and offer customized conversational customer support. By integrating these solutions with IVR systems, companies can automate customer interactions, facilitate smart and personalized self-service by providing voice responses in the customer's language and remove communication barriers. Furthermore, organizations can also use text to audio converters to make AI-enabled routine calls to inform customers about promotional offers, payment reminders, and much more. That said, by using text-to-speech in voice-activated chatbots, businesses can provide customers, especially the visually impaired, with a more immersive experience, thereby enriching the customer experience.

Global market penetration

Text to speech online solutions offer synthetic voices in multiple languages enabling businesses to create content in several different languages and reach customers across different countries worldwide. Organizations can build trust with customers by creating voiceovers for ads, commercials, product demos, explainer videos, and PowerPoint presentations, among other content pieces in regional dialects and native languages. 

Increases Web Presence

That said, with the help of text to audio generators, businesses can provide an audio version of their content in addition to a written version, enabling more accessibility to a broader audience, who can choose whether to read or listen to it based on their preferences. This increases the brand's web presence. Moreover, using text-to-speech, brands can create a familiar, recognizable and unique voice across all their voice channels, making it easy for customers to identify the brand the second they hear it. This way, the brand shows up everywhere and improves its web presence.

Who else can benefit from text to speech tools?

Today’s online text to speech systems can generate speech that is almost indistinguishable from a human voice, making them a valuable tool for a wide range of applications, from improving accessibility for people with disabilities to providing convenient and efficient ways to communicate information.

Here is a list of everybody that can benefit immensely from using best text to speech softwares for their content and voiceover needs:

Many educators struggle to enhance the value of their curriculum while simplifying their workloads. This is where realistic text to speech technology plays a key role. Firstly, it improves accessibility for students with disabilities. Screen readers and other tools which are speech enabled can make learning an equal opportunity and enjoyable experience for those with learning and physical disabilities. Secondly, it helps teach comprehension in an effective manner. Text to speech software offers an easy way for students to listen to how words are spoken in their natural structure and following the same is easier through audio playback.

TTS software also enhances engagement and makes learning interesting for students. For example, using natural sounding text to speech voices, teachers can create engaging presentations and elearning modules that capture student’s attention. 

In marketing specifically, text to speech technology can help improve data collection, facilitate comprehensive customer profiling, and better data analysis. Online text to speech tools offer an easy way for businesses to reach a broader audience and create customized user experiences.

For instance, marketing teams can create and deliver videos to prospective clients to establish a connection and brief them on queries and complicated products or services in the language and accent the customer is comfortable with. Furthermore, AI voices enable marketing teams to create crisp, high quality professional-sounding voiceovers in a few simple steps without hiring voice actors or requiring any professional recording studios.

Text to speech generators offer authors numerous advantages. One, it serves as an editing aid and helps storytellers proof read their novels and manuscripts to identify grammatical errors and other mistakes in their drafts before publishing. Listening to their stories being read aloud also allows authors to gauge the response to their work on other people. Authors can also use realistic voice generators to convert their books into audiobooks and podcasts and broaden the reach of their work. 

From interviews about true crime to politics and science, there are all sorts of popular podcast formats today. And, regardless of how good your podcast topic is, it won’t matter if the host doesn’t have a good voice. That said, not everyone can have that best podcast voice like an old-school radio anchor or a news presenter. This is where text to speech platforms come in. You don’t have to record scripted intros, prologues, or epilogues, an AI narrator can do it for you. Through text to speech software, you can automatically create the narrative and voiceover for your podcast in the language and tone you want in a matter of minutes by simply uploading the script to the platform. 

Creating good voice overs for your animated explainer videos or product demos or games typically meant investing a lot of money on recording equipment and hiring professional voice actors. Not anymore. With AI text to speech platforms, you can add natural sounding voices to your animated video to make them more engaging and captivating. In fact, with text to speech software, you can give each character in your animated video or game, a unique voice. 

Customer Support Executives

Integrating realistic text to voice software with an IVR system enables customer service agents to concentrate more on complex customers rather than common queries. TTS-enabled IVR systems are capable of gathering information and providing responses to customers as necessary in a way that sounds just like an actual customer service agent.

Furthermore, text to voice systems also eliminate the need for IVR businesses to schedule voiceover retakes months in advance. With TTS systems, businesses can render a new voiceover in minutes creating thousands of iterations within a few clicks.

Text to speech reader is a game-changer for students of all ages and educational levels. By converting written text into spoken words, students can enhance their learning experience and comprehension. Text to speech technology can read content out aloud, making it easier for students to absorb information while multitasking. It is particularly useful for students with dyslexia, ADHD, or other learning disabilities as it provides them with an alternative way to consume educational content. Furthermore, the TTS tool can also be used to add narrations to presentations, explainer videos, how-to videos, and more.

Be it corporate trainers, fitness trainers, or lifestyle instructors, text to speech can be used to create engaging and accessible learning materials. For example, fitness trainers can convert written content into audio-based workout routines and personalized exercise plans. This helps to increase engagement levels and knowledge retention among the audience.

Similarly, corporate trainers can also use text to speech converters to create presentations on employee policies and other organizational practices. It makes the coursework highly engaging and improves employee performance at many levels. Additionally, using audio course materials is a great way to respect the staff with disabilities and give everyone equal access to training.  

Content Creators 

Content creators, including social media users, bloggers, writers, influencers, and authors, can leverage text to speech to enhance their productivity and reach a broader audience.

This technology enables content creators to convert their written articles, scripts, blog posts, or eBooks into high-quality audio files quickly in multiple languages instead of manually recording the voiceover.

Consequently, it opens up new avenues for content consumption. This allows readers to listen to the content while performing other tasks or when reading isn’t feasible, such as during commutes or workouts. 

Video Producers 

Video creators can easily add voiceovers or narration to their videos, eliminating the need for hiring voice actors or spending hours recording audio. This not only saves time and resources but also ensures consistent and professional-sounding voiceovers.

Murf: The Ultimate AI Text to Speech Software

If you are looking for a text to speech generator that can create stunning voiceovers for your tutorials, presentations, or videos, Murf is the one to go for. 

Murf can generate human-like, realistic, and natural-sounding voices. Its pièce de résistance is that Murf can do it in over 120+ unique voices in 20+ languages. 

This TTS reader also allows you to tweak the pitch of the voice, add pauses or emphasis, and alter the speed of the output to get the output just the way you want it. 

And the best part? Murf is extremely easy to use. Just type or paste in your script, choose your preferred voice in the language you want, and hit play. Murf will do the rest. 

Create Engaging Content with Murf's AI Voices

Murf text to audio converter can be used in a number of scenarios to elevate the quality of your overall content. Let's look at a few use cases where Murf can help and why it’s the best text to speech reader out there:

E-learning Videos

Murf’s free text to speech reader can help you create e-learning videos in multiple languages that will make your content accessible to a global audience. You can also increase the engagement of your e-learning video by adding emotions and expressions to your content. 

Presentations

Murf’s AI voices can add a touch of professionalism to your presentations to help drive home those key points. You can use Murf to narrate your slides, explain your concepts, or tell the story of your brand in the exact tone and style you envisioned. 

You can also use this free text to speech reader to make your audiobooks sound as if they its been narrated by an actual person.

With Murf, you can also mix and match different voices for the various characters in the audiobook to take your storytelling up a few notches. 

Sales and Marketing Videos

Murf can also enhance your sales and marketing videos with persuasive and professional voiceovers. You can use these videos to showcase your products, services, or offers and tailor them in multiple languages to advertise to a potentially global audience. 

Product Demos

Finally, Murf can help you create informative and engaging product demo videos that showcase your product’s features and benefits in the best possible light.

Key Features of Murf AI Text to Speech

Apart from enabling users to enhance the quality of their voiceover content with compelling, nuanced, and natural sounding text to speech voices,  Murf offers an intuitive voice user interface and the ability to customize and control the voiceover output with features like pitch, speed, emphasis, pause, pronunciation and more.

More than Just a Text to Speech Software

Tired of hearing monotonous, robotic-sounding voiceovers? Not anymore. With Murf, enhance the quality of your content with compelling, nuanced, and natural sounding text to speech that replicate the subtleties of human voice. Fine-tune your voiceover narration and add more character to an AI voice with features such as Emphasis, Pronunciation, Speed, and more! From inviting and conversational to excited and loud to empathetic and authoritative, we have AI voices that span different intonations and emotions. Murf AI text to speech (TTS) supports Arabic, Chinese, Danish, Dutch, English, Finnish, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Portuguese, Romanian, Russian, Spanish, Tamil, and Turkish. Some of these languages also support multiple accents. For example, our English language AI voices support British, Australian, American, and Indian accents. Our Spanish AI voices support Mexican and Spain accents. The TTS online software also offers users the ability to add background audio or music to their content. Murf studio, in fact, comes with a curated selection of royalty-free music in their gallery that the user can choose from to add some music to their video. You can also upload your own audio files or even import from external sources like YouTube, Vimeo, and other video websites. Murf's text to sound has a voice changer feature that lets you upload your existing recording and revamp it with professional AI voice in a single click. You can change your voice to an AI voice in three simple steps: transcribe the audio, choose an AI voice, and regenerate the audio in a new voice. It's as easy as pie.

Additionally, the tool also supports an AI translation feature that enables you to convert your scripts and voiceovers into multiple languages in minutes. With Murf AI Translate, you can convert your projects into 20 different global and regional languages, making them accessible to a broader audience and expanding your reach.

Summing It Up

Murf is a powerful text to speech reader that can help you create engaging and professional voiceovers for your videos, presentations , and so much more. 

To put it in short, with Murf, you can:

• Save a ton of money that would have otherwise been spent on voice actors and renting out studio spaces.
• Widen your reach to a global audience with its support for over 120+ unique voices in over 20+ languages.
• Make your content accessible to anyone with visual or specific cognitive disabilities. 

So, what are you waiting for? Sign up for a free trial of Murf today!

Murf supports Text to speech in

Important Links

How to create.

Free Text to Speech Online with Natural Voices

Convert Texts, PDFs & Ebooks to Speech Voices As You Like

• Up to 40+ language & 100+ voices
• Online voice generator
• High quality natural sounding
• Fast, easy & free

From Text to Speech, NaturalVoicer is the Best

If you need to generate voices for YouTube, Vimeo, PPT or a website, or simply have difficulty reading large amounts of text, you should definitely consider Naturalvoicer. Fast, easy, and natural-sounding, it's the perfect online voice generator.

Supports Multi-language

While most TTSreaders only support English, we have 50+ languages supported, like French, Portuguese, German, Japanese, Chinese, etc.

Support Multiple File Formats

You can upload pdf, txt, doc(x), epub and convert all these files into audio file. You can listen on the go or download MP3.

Natural Sounding Voices

We provide the most human-like, high-fidelity, accurate voices, including male and female voices in different accents or tones.

Free for Commercial Use

You can use our voices free for commercial usage, like making voice overs for Youtube videos, or creating eLearning course materials.

Playback, Speedup & Pause

You can choose between diffrerent reading speeds, pause or stop speaking, continue from where you stopped last time.

100% Free and Safe

We provide free TTS online services (Characters < 3000/day). For keeping security, we will never save any audios.

What Can NaturalVoicer Help with

NaturalVoicer makes your life easier and more interesting.

People with Dyslexia

I have severe dyslexia. Reading has always been a challenge for me. Thanks to Naturalvoicer, I finally don’t have to endure the pain of staring at the phone screen all the time, which makes me more efficient than before.

Students & Learners

I am a bilingual graduate student. I found NaturalVoicer to be a great tool to help me complete the course. I will upload my coursework and let NaturalVoicer read back what I wrote, to help me proofread the content a second time. To be honest, it is easier to find and correct mistakes than to read it word by word.

Video Makers

As a YouTuber whose native language is not English, I seldom make voices in my videos before. BTW, my videos are mainly about smartphone reviews. Not talking really sounds strange, so there are little followers. Now with NaturalVoicer, I can add authentic voiceovers, and my number of followers and interaction rate have also increased.

Multitaskers

My commute time is very long, which takes three hours to drive. When I want to catch up on the news, podcasts and audiobooks, I will convert some periodicals and e-books into MP3 with NaturalVoicer and then listen on the go. This stimulates my unlimited imagination and greatly reduces my fatigue during driving.

Visually Impaired Readers

I have severe floaters, so reading has always been a very difficult thing for me. My daughter guided me to use NaturalVoicer. It opened up a whole new world for me. I can choose what I need to know and it read out. This is a very valuable tool and I highly recommend it to anyone with vision problems.

International Marketer

As an international salesman, it is not a rare thing to meet customers from other countries. Thanks to NaturalVoicer, it has more than 40 languages to help me handle daily cases and dialogues. So far I have successfully developed clients in France and Spain.

Frequently Asked Questions

What is text to speech.

A text-to-speech tool, also known as a text reader or text-to-speech software, is a technology for reading digital text aloud.

It was originally an assistive technology developed for the visually impaired to help them understand and read texts on a mobile phone or computer. Now it is also widely used in other fields, such as making eLearning courseware, YouTube voiceovers, etc.

Which languages are supported by NaturalVoicer?

We support US English, UK English, Spanish, Spanish Latin American, French, French Canadian, Arabic, Portuguese, Chinese, Chinese HongKong, Chinese TaiWan, Hindi, Brazillian, Aemenian, Australian English, Bangla Bangladesh, Bangla India, Czech, Danish, Deutsch, Dutch, Estonian, Fillipion, Finnish, Greek, Hungarian, Indonesian, Italian, Janpanese, Korean, Latin, Netpali, Norwegain, Polish, Romanian, Russian, Sinhala, Slovak, Swedish, Tamil and more.

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Why there is no sound when I click play?

• Check your speakers and volume.
• Check your microphone and permission for accessing the microphone.
• If there is still no sounds, please contact us describing the problem in detail.

Mail to: [email protected]

There is a 2000-characters limit, how can I get more characters?

Each user can only convert 2000 characters per day for free. If you need more characters, please upgrade to our premium plans (0.1million characters/$9.99, 0.5million characters/$19.99). Or wait till next day.

More FAQ >>

Voice Selection

language and regions

GraysonV2 - English

• Voice Settings

Advanced Settings

Voice Volume

Voice Speed

Write something to convert!

Text to Speech

Realistic Voices

Completely Free

Multi language

TTSVox Use Cases

Enhance your videos with lifelike TTSVox voices for engaging narration and commentary.

Transform e-learning courses with natural voices for accessible and immersive education.

IVR Systems

Upgrade IVR systems with clear, natural voices for improved customer service experiences.

Audio Articles

Turn articles into audio with TTSVox: Engage more listeners with accessible, voice-powered content.

Revolutionary Text to Speech Feature

Experience the future of content consumption with our Text to Speech feature, transforming text into natural, lifelike audio for an enhanced listening and learning experience.

Lifelike, Realistic Voices for Your Content

Our TTS software offers a range of realistic voices, meticulously designed to replicate human nuances, ensuring your audio content is engaging, natural, and authentic for all audiences.

Enjoy Completely Free Text to Speech Services

Unlock the power of voice with our completely free Text to Speech service, offering unlimited access to high-quality, lifelike audio conversion without any hidden costs.

Multi-Language Support for Global Reach

Broaden your audience with our Text to Speech software, featuring multi-language support to bring your content to life in various languages, ensuring inclusivity and global accessibility.

frequently ask questions

What is text to speech (tts) and how does it work.

Text to Speech (TTS) is a type of assistive technology that reads digital text aloud. It's a valuable tool for individuals with visual impairments or reading disabilities, as well as for those who prefer auditory learning or need hands-free reading. TTS works by converting written text into spoken words using a computer-generated voice. With advanced TTS online platforms like TTSVox, users can input any text and have it instantly transformed into natural-sounding audio, enhancing accessibility and convenience for educational, professional, and personal use.

Is TTSVox free to use for converting text to speech?

Yes, TTSVox is a completely free text to speech online tool that allows users to convert any text into high-quality spoken words. Our platform is designed to be accessible to everyone, offering a user-friendly interface and instant conversion without the need for any downloads or installations. Whether you're a student, professional, or simply looking for a TTS solution for personal use, TTSVox provides an efficient and cost-effective way to bring your text to life.

Can I customize the voice and language in TTSVox?

Absolutely! TTSVox offers a wide range of voice options and supports multiple languages, allowing you to customize the output to fit your specific needs. Whether you're looking for a particular accent, gender, or tone, our TTS online tool provides the flexibility to select the perfect voice for your text. This feature makes it ideal for creating diverse and engaging audio content for audiences worldwide.

How accurate is the text to speech conversion with TTSVox?

TTSVox is dedicated to providing highly accurate and natural-sounding text to speech conversions. Our platform utilizes advanced speech synthesis technology to ensure that every word is pronounced clearly and accurately. We continuously update our algorithms to improve the quality and naturalness of the audio output, making it one of the most reliable TTS online tools available today.

What are the benefits of using an online TTS tool like TTSVox?

Utilizing an online TTS tool like TTSVox brings multiple advantages, including enhanced accessibility for individuals with reading difficulties or visual impairments by converting text to audible speech, offering unparalleled convenience for users to consume information while multitasking or on the move. The platform's wide range of customizable voice and language options provides a tailored listening experience, catering to diverse user needs. Moreover, TTSVox stands out as a cost-effective solution, eliminating the need for expensive software or hardware, making it ideal for educational purposes, professional use, and personal enjoyment. Its commitment to high-quality, natural-sounding speech synthesis technology ensures a reliable and engaging auditory experience, promoting better comprehension and accessibility of written content for a global audience.

AI Voices every language in the world

Generate realistic Text to Speech (TTS) audio using our online AI Voice Generator and the best synthetic voices. Instantly convert text in to natural-sounding speech and download as MP3 and WAV audio files.

canada english

USA English

british english

irish english

Free AI Voice Generator

Use Deepgram's AI voice generator to produce human speech from text. AI matches text with correct pronunciation for natural, high-quality audio.

AI Voice Generation

Discover the Unparalleled Clarity and Versatility of Deepgram's AI Voice Generator

We harness the power of advanced artificial intelligence to bring you a state-of-the-art AI voice generator designed to meet all your audio creation needs. Whether you're a content creator, marketer, educator, or developer, our platform offers an incredibly realistic and customizable voice generation solution.

Human Voice Generation

Our AI voice generator is engineered to produce voices that are indistinguishable from real human speech. With a vast library of voices across different genders, ages, and accents, Deepgram empowers you to find the perfect voice for your project.

Low-latency Text to Speech

Deepgram's voice generator is one of the fastest on the market. We design our AI models to produce high-quality voices

How It Works

Choose Your Voice : Select from our diverse library of high-quality, natural-sounding AI voices.

Generate: Enter your text, generate your voiceover in seconds.

Download: Once you have you AI generated speech, easily download your audio file.

AI Voice Generator Use Cases

E-Learning and Educational Content : Create engaging and informative educational materials that cater to learners of all types.

Marketing and Advertising : Enhance your marketing materials with high-quality voiceovers that grab attention.

Audiobooks and Podcasts : Produce audiobooks and podcasts efficiently, with voices that keep your audience engaged.

Accessibility : Make your content more accessible with voiceovers that can be easily understood by everyone, including those with visual impairments or reading difficulties.

See the most popular languages and voices. Learn more →

Free text to speech over 200 voices​ and 70 languages

Luvvoice provides a complimentary online service that converts text into speech(TTS) for free. Simply input your text, choose a voice, and either download the resulting mp3 file or listen to it directly.

Everything you need

What are the features of Luvvoice ?

Built on deep learning and Ai breakthrough research to generate sounds that are extremely close to the quality of real human voices.

A large number of high-quality voices, 200 voices in more than 70 languages, your best text reader.

Copy-paste an existing script or type in the text for your script on text editor. Choose an AI voice of your choice from Luvvoice’s library of voices .

best tts tool

The most powerful creative and business tools

Luvvoice can generate a variety of character voices that you can use in marketing, and social media such as Youtube and Tiktok, you can use to learn new languages and read books aloud!

Most Popular Languages and TTS Voices We Support

Easily convert text into audio, choose your favorite language and voice:

⭐️⭐️⭐️⭐️⭐️ Nice work on Luvvoice. This is a very good text reader! If you aren’t sure, always go for Luvvoice. Believe me, you won’t regret it. Olivia Walker Consultant

⭐️⭐️⭐️⭐️⭐️ Really good. Luvvoice is by far the most valuable business resource we have ever purchased. I love this TTS tool. Ashley Taylor Blogger

Frequently asked questions

Yes, Luvvoice is completely free to use.Free text to speech over 50 language and 200 voice,no words limit. Listen online and download files in mp3 format.

Text-to-Speech (TTS) technology converts text into natural-sounding speech. Learn more about TTS.

Converting text to speech is easy. Simply paste or type the text into the designated text box, choose the language for the text and your preferred voice style, and click the ‘Submit’ button to initiate the process. The text will be processed, and you can download the audio file.

Yes, all voices from Luvvoice are suitable for commercial projects such as videos, podcasts, gaming characters, Youtube and TikTok, and you are not required to attribute the source.

Free text to speech tool

How to use our text to speech (tts) tool.

A text-to-speech reader has the function of reading out loud any text you input. Our tool can read text in over 50 languages and even offers multiple text-to-speech voices for a few widely spoken languages such as English.

• Step #1 : Write or paste your text in the input box. You also have the option of uploading a txt file.
• Step #2 : Choose your desired language and speaker. You can try out different speakers if there are more available and choose the one you prefer.
• Step #3 : Choose the speed of reading. You can set up the text to be read out loud faster or slower than the default.
• Step #4 : Choose the font for the text. We recommend a smaller font if you have a large text and want to avoid scrolling, or a bigger font to follow the text while easily read aloud.
• Step #5 : Tick the “I’m not a robot” checkbox in the bottom right of the screen.
• Step #6 : Press the play button on the bottom of the text box to hear your text read out loud.
• Step #7 : Get a share link for the resulting audio file or download it as an mp3. Our tool generates high quality TTS that is easy to understand by everyone.

Choose from 50 languages

Our free text to speech tool offers various languages and natural sounding voices to choose from. We made an effort to make our TTS reader available for as many people as possible by including the most commonly spoken languages worldwide.

We have languages available for the following regions:

• Middle East
• South-East Asia
• Middle Asia (India)
• North America

Benefits of using text to speech

TTS is widely used as assistive technology that helps people with reading and visual impairments understand a text. For example:

• Visually impaired individuals greatly benefit from having a program read texts out loud to them.
• Dyslexic individuals will also benefit from a text to talk reader because they can understand texts more easily.
• Children with reading impairments can use text readers to understand lessons easier.
• A text to voice tool is also of great help for people with severe speech impairments. Our web browser TTS tool allows them to type what they want to say and instantly play the audio to the person they wish to communicate with.

Other benefits of reading text aloud:

• People learning or communicating in non-native languages can use text to speech as a tool for learning how to spell words correctly and express themselves fluently in their desired language. It’s beneficial when traveling to a country where that language is spoken, and one wants to communicate with locals in their native language.
• Younger people in multilingual families might find it challenging to communicate with grandparents who still reside in their native countries. Text to speech can bridge the linguistic gap and help strengthen family bonds.
• Muti-taskers and busy people, in general, can use text to speech online to get the latest news.

What is text to speech?

Text to speech is a tool or program that takes text or words input by the user and reads them out loud. It’s used as an assistive technology for people with reading, visual and speech impairments and as a productivity tool.

How does text to speech work?

Text to speech tools use speech synthesis to read texts out loud. The simplest form of speech synthesis uses snippets of human speech to deliver a coherent and natural-sounding message. These snippets are taken from vast libraries of human sounds, words, phrases etc., and they can be used to verbalize almost anything digitally.

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Explore our range of complimentary tools designed to enhance your experience.

Grow revenue and improve engagement rates by sending personalized, action-driven texts to your customers, staff, and suppliers.

Text to Speech Human Voice

Convert text to speech with human voice online; free

Online instant text-to-voice reader

Use our human voice text to speech software to convert your text to voice and add it to any video online! Just type or paste your text, select a voice that you want to use, and hear your text being read aloud by our AI! You can also create an animated talking AI avatar for your video. Use our text-to-speech avatars for your content. Or you can just download your project as an audio file! You can even add music to your voiceovers, sound effects, and more!

How to convert text to human voice:

1 upload or record.

Upload your video to VEED or start recording using our free webcam recorder. You can also generate content from prompts using our AI text-to-video tool.

2 Add text and convert to voice

Click Audio from the left menu and select Text to Speech. Type or paste your text into the text field and click Add to Project. You will see an audio file in the timeline.

Export your text-to-speech video or audio. Or keep exploring our AI video editing tools to make your content as engaging as possible.

Learn more about text-to-speech in this video:

Online instant text reader

You can use VEED’s free text reader online—straight from your browser. No need to download complicated and expensive apps. All you have to do is type your text or paste a text you’ve copied into the text field, and add the audio file to your project. Or replace your original spoken audio with a translated voiceover—automatically using our voice dubber ! You can download your video when you’re done or if you only need the voice over, just export the audio from your project.

AI-generated human voice profiles

Our text to speech human voice online software lets you select from different male and female voice profiles—to read your text aloud. You can preview the voice so you can hear how it sounds before adding it to your video. Guaranteed that your text will be read by a human voice. You can also use your voice profile to add instant narrations using the AI voice cloning tool.

All-in-one video and audio editor

Not only can you convert text to human voice online, but you can also use all of VEED’s video editing tools to create professional-looking videos in just a few clicks. You can add animated text, add images, subtitles, emojis, and drawings to your video. Use our camera filters and special effects to make your videos look stunning.

Upload your video to VEED or record one using our webcam recorder. Click Audio from the left menu and start typing or pasting your text. Select a voice, preview the speech, and add it to your video! It’s that simple.

All our voice profiles in our selection sounds like real humans—and not like the robotic voiceovers you mostly hear on TikTok. Our AI text-to-speech generator uses real voice actors!

VEED’s text-to-voice software is free to use. You can convert your text into a video or even an audio file, and you can do it straight from your browser.

Currently, you can add up to 1,000 characters to convert to speech per video project.

Discover more

• Afrikaans Text to Speech
• AI Voice Generator
• AI Voice Over
• Amharic Text to Speech
• Arabic Text to Speech
• Audiobook Maker
• Bangla Text to Speech
• Cantonese Text to Speech
• Chinese Text to Speech
• Convert Articles to Audio
• English Text to Speech
• French Text to Speech
• German Text to Speech
• Hebrew Text to Speech
• Hindi Text to Speech
• Irish Text to Speech
• Italian Text to Speech
• Japanese Text to Speech
• Korean Text to Speech
• Lao Text to Speech
• Malayalam Text to Speech
• Persian Text to Speech
• Realistic Text to Speech
• Russian Text to Speech
• Somali Text to Speech
• Spanish Text to Speech
• Speech in Swahili
• Tamil Text to Speech
• Text Reader
• Text to Audio
• Text to Podcast
• Text to Speech Bulgarian
• Text to Speech Catalan
• Text to Speech Converter
• Text to Speech Croatian
• Text to Speech Czech
• Text to Speech Danish
• Text to Speech Dutch
• Text to Speech Estonian
• Text to Speech Finnish
• Text to Speech Greek
• Text to Speech Gujarati
• Text to Speech Hungarian
• Text to Speech Khmer
• Text to Speech Latvian
• Text to Speech Lithuanian
• Text to Speech Malay
• Text to Speech Marathi
• Text to Speech MP3
• Text to Speech Norwegian
• Text to Speech Polish
• Text to Speech Portuguese
• Text to Speech Romana
• Text to Speech Serbian
• Text to Speech Slovak
• Text to Speech Slovenian
• Text to Speech Swedish
• Text to Speech Tagalog
• Text to Speech Telugu
• Text to Speech Thai
• Text to Speech Turkish
• Text to Speech Ukrainian
• Text to Speech Voice Changer
• Text to Speech with Emotion
• Text to Talk
• Text to Voice Generator
• Text to Voice Over
• Urdu Text to Speech
• Vietnamese Text to Speech

What they say about VEED

Veed is a great piece of browser software with the best team I've ever seen. Veed allows for subtitling, editing, effect/text encoding, and many more advanced features that other editors just can't compete with. The free version is wonderful, but the Pro version is beyond perfect. Keep in mind that this a browser editor we're talking about and the level of quality that Veed allows is stunning and a complete game changer at worst.

I love using VEED as the speech to subtitles transcription is the most accurate I've seen on the market. It has enabled me to edit my videos in just a few minutes and bring my video content to the next level

Laura Haleydt - Brand Marketing Manager, Carlsberg Importers

The Best & Most Easy to Use Simple Video Editing Software! I had tried tons of other online editors on the market and been disappointed. With VEED I haven't experienced any issues with the videos I create on there. It has everything I need in one place such as the progress bar for my 1-minute clips, auto transcriptions for all my video content, and custom fonts for consistency in my visual branding.

Diana B - Social Media Strategist, Self Employed

More than a text-to-speech human voice software

With VEED, you can do a lot more than just convert text to human voice. It’s a complete professional video-editing software that lets you create stunning videos in just minutes. You don’t need any video editing experience. Plus, you can make use of our video templates; create videos for your business or personal use. Create sales videos, movie trailers, birthday videos, and so much more. Try VEED today and create stunning videos straight from your browser in just a few clicks!

Turn any text into audio instantly

Listening was Born Before Reading

Listening predates reading in human communication history and remains a natural and intuitive way to absorb information.

Natural-Sounding Voices

The AI Text-to-Speech (TTS) technology powers our free reader with high-quality voices so you can enjoy the timeless advantages of listening.

Do More with Your Time

With our app, you can get through documents, articles, PDFs, and emails effortlessly, freeing your hands and eyes.

Listen to Anything, Anywhere

You can listen to any text on desktop or mobile devices. Use our app now and unlock the potential of listening as the ultimate reading companion.

Select your Speechise Plan

Start free, upgrade when you need

Guaranteed safe & secure checkout

Frequently Asked Questions

If you don't find your answer here, please   contact us .

How does Speechise work?

You just open speechise.com in a browser, paste your text and click Play. The system converts the text to audio and the sound starts almost immediately. The chunk of text that is currently playing is highlighted in your browser. You can pause or continue listening.

Is Speechise Free?

Yes, you can use Speechise for free with the limit of 2,000 characters per single request.

All our subscription options are listed on the pricing page   for your convenience. You can upgrade to a paid version if you like Speechise and want to use it fully. Your feedback is appreciated in any case.

What Languages are Supported?

You can use 50+ languages and variants in 380+ voices.

Some of the supported languages are English, Spanish, Portuguese, French, German, Turkish, Italian, Dutch, Norwegian, Polish, Swedish, Bulgarian, Czech, Hungarian, Finnish, Greek, Ukrainian, Russian, Arabic, Korean, Hindi, Japanese, Chinese, Thai.

What is text-to-speech (TTS)?

Artificial intelligence (AI) software reads text or a document aloud for you. The text can be a fragment or a PDF, eBook, email or a webpage. The language can be English, Spanish, Portuguese or other. The voice sounds human and you can select accent/character.

Do I need to install anything?

No installation required. Speechise simply works in your browser on a desktop computer or a mobile device.

Realistic Voice AI

Lifelike and Powerful AI-Powered Free Online Text to Speech

Try the tool (any language)

Free Text To Speech Reader

• 1 Select voice John Kelly
• 2 Select talking speed 0.5 0.6 0.7 0.8 0.9 Normal Speed 1.1 1.2 1.3 1.4 1.5 2.0 3.0
• 3 Select pitch +1.8 +1.7 +1.6 +1.5 +1.4 +1.3 +1.2 +1.1 1.0 -0.9 -0.8 -0.7 -0.6
• Vocalize Vocalizing
• Download Vocalizing

Examples of text-to-speech translation

About VoxWorker.com

What is voxworker, multiple languages, variety of voices, file formats, easy to use, usage options.

Text to Voice Generator

Let our text to voice generator do the talking

Need a voiceover for your next project? Kapwing's text to voice generator allows you to apply AI-powered voices to your projects with just a bit of text. In just a few clicks, you'll be able to generate a realistic-sounding voice that will read your text exactly as provided.

Once you've converted your text into speech, you can easily make edits or export the audio to popular formats like MP3. There's no software to download or plugins to install—our text to voice tools work right inside your browser. Just click the Get Started button above and write or import your text.

How to generate voice from text

• Open Text to Speech settings Click on the “Audio” tab on the left-hand side and select “Text to Speech” to open the text to speech tab.
• Personalize your voice Once your text is added, use the dropdown menus to select language and voice. When you are satisfied, click Generate Audio Layer.
• Export file When you're finished, click 'Export Project' in the top right to export and download files to any device.

Realistic-sounding voices powered by text

Automatic text to voice for everyone.

Take your content further by using our text to voice tool to apply voice overs to any video. Once you've generated a voice from your text, handle the rest of your video production with Kapwing's background noise remover and audio editing tools. We're the internet's #1 free video editor for a reason.

Human-sounding, AI generated

Our text to voice tools are powered by robots but sound casual and natural. Human voices in both male and female are available, and you can even fine-tune the audio with our built-in sound effects and stock music. Your text will be spoken naturally so your voiceovers feel polished.

Fast, accurate voiceovers from your browser

Your text is spoken aloud exactly as it's written—no obvious robo-voices and every line is said in a natural tone. There's no software or plugins to download; simply add your text and Kapwing will auto-magically create a human-sounding voice. Once you're done, export your files in seconds.

Make all of your videos more accessible

Content gets read, watched, and listened to when it's presented in a viewers' favorite format. Voice generators make it easy for people who are too distracted to watch your video to engage with your content. And with our text to voice converter, you can add spoken voices to any video, any time.

Frequently Asked Questions

How do I turn my text into a voice?

What's the best voice generator for text to speech, how can i use text to voice in videos, what's different about kapwing.

Kapwing is free to use for teams of any size. We also offer paid plans with additional features, storage, and support.

LIMITED TIME OFFER: For a limited time, enjoy 50% off on select plans.

AI Voice Generator: Most Realistic AI Text to Speech

Hyper realistic ai voice generator that .css-1625k06{background:var(--chakra-colors-transparent);white-space:nowrap;background-image:linear-gradient(to right, var(--chakra-colors-blue-600), var(--chakra-colors-skyblue-600));color:transparent;-webkit-background-clip:text;background-clip:text;} captivates your audience.

Join the over 2,000,000 users who love LOVO AI. Our award-winning voice generator and text to speech software is packed with 500+ voices in 100 languages. Create engaging videos with voice for marketing, training, social media, and more!

Start now for free

Chloe Woods

English Female

Sophia Butler

Santa Clause

English Male

Katelyn Harrison

Bryan Lee Jr.

Thomas Coleman

Create and edit videos effortlessly with Genny’s all-in-one voice and video editing platform.

Trusted by professionals & creatives globally

Introducing Genny The best way to add voiceover to video

Experience unparalleled voiceover production with our voice generator and online video editor,  featuring professional grade human-like voices and powerful editing tools.

The most natural voices in the world

Surprise your audience with the perfect AI voice in 100+ languages for your content.

Genny is the .css-1ezzeyz{background:linear-gradient(90deg, #2871DE 0%, #27AADC 100%);white-space:nowrap;color:var(--chakra-colors-transparent);-webkit-background-clip:text;background-clip:text;-webkit-background-clip:text;-webkit-text-fill-color:transparent;} ultimate generative AI tool

For all your voiceover and video needs - scripts, ultra-realistic voices, images, editing and more! Genny has all the features you need to create engaging videos with integrated AI features.

Save $$ and time on voiceovers

Using Genny removes the need to spend time and money to record or use expensive equipment to achieve professional voiceovers with our advanced voice generator.

Text To Speech

Sync audio and video seamlessly

Achieve perfect synchronization without sacrificing speed or accuracy. With Genny’s online video editor, you can edit content effortlessly to create engaging high-quality videos.

Online Video Editor

Boost engagement with subtitles

Globalize your content and boost engagement in 20+ languages with our auto subtitle generator. Customize, animate, and transform your video with just a few clicks.

Auto Subtitle Generator

Write scripts 10x faster

Writer's block is everyone's nightmare. Genny's AI writer can help you get started on your script quickly by generating professionally written content in a lightening fast.

Create unique voices in minutes

Genny’s voice cloning lets you instantly create custom voices with just one minute of audio. Give your brand a unique voice that sets your content apart from the crowd.

Voice Cloning

Generate royalty-free images

No more spending hours searching the web for the perfect stock image. Generate HD royalty-free images and add them to your videos in seconds with Genny’s AI art generator.

AI Art Generator

.css-bd7824{background:linear-gradient(90deg, #2E94FF 0%, #408CFF 32.81%, #3DB5FF 71.35%, #2ED1EA 100%);white-space:nowrap;color:var(--chakra-colors-transparent);-webkit-background-clip:text;background-clip:text;-webkit-background-clip:text;-webkit-text-fill-color:transparent;} Collaborate with your team

Drive efficiency and collaborate creatively with Genny teams and keep your projects safely secured with our cloud storage so you and your team can access them at any time!

Learn About Genny Teams

.css-1pdu0yo{background:var(--chakra-colors-transparent);white-space:nowrap;background-image:linear-gradient(90deg, #2E94FF 0%, #408CFF 32.81%, #3DB5FF 71.35%, #2ED1EA 100%);color:transparent;-webkit-background-clip:text;background-clip:text;webkit-background-clip:text;webkit-text-fill-color:transparent;} Versatile API made for developers

With our easy to use API, you now have the power to use the most advanced AI voices in the world in your own app or service! Get started in as little as 5 lines of code.

LOVO Open API

AI Voice Generator for any use case

Unlock your creative potential

Try Genny for free

Create a free voiceover

Start .css-l9o03z{background:var(--chakra-colors-transparent);white-space:nowrap;color:var(--chakra-colors-blue-600);} saving 90% of your time and budget today!

See pricing

No Credit Card required

14-day trial of pro

You might find an answer faster here

If you cannot find an answer, email [email protected] for help.

What happens if I hit my credit limit?

What does "Voice Generation Hours" Mean?

How is LOVO different from other TTS?

Can I use LOVO for Youtube videos?

Do I own the rights to content created?

What is an AI voice?

Which languages do you support?

Which emotions can LOVO express?

Do you have an API?

Do you have an enterprise plan?

Can I cancel any time?

What is an AI voice generator?

Check out latest articles on our blog

6 Benefits of Real-Time Voice Cloning

Effective Text To Speech Tools For Instructional Design

Most Popular AI Voiceover Apps For TikTok

Best AI tools for businesses and marketers

Voice generators - perfect for content creation

LOVO is the most advanced AI voice and text-to-speech generator available on the market. With LOVO, you can save thousands of dollars and hours of time in generating realistic and high-quality voiceovers. Our cutting-edge technology produces super realistic voices that are almost impossible to distinguish from real human voices. Our easy-to-use professional UI makes generating voiceovers effortless, even for those with no prior experience in audio production. LOVO is perfect for businesses, content creators, educators, and anyone looking to create engaging content that stands out from the crowd. LOVO is designed to streamline your content creation process so you can focus on what matters most - delivering your message to your audience. With LOVO, you have access to an extensive library of voices, languages, and accents, ensuring that you find the perfect voice to match your brand or project.

Here are just some of the reasons why LOVO’s is the perfect tool for content creation

Scale content without scaling costs or resources.

With AI now more accessible than ever, tools like text-to-speech generators are the perfect assistant for content creation. These tools save you time and money by removing the need for expensive equipment or time-consuming tasks such as recording and editing while providing high-quality audio with realistic human voices.

Produce professional-grade content

At LOVO, our team has focused on creating Genny, the most advanced voice generator that produces high-quality voiceovers to elevate your video and audio projects. Complete the final stages of your project with Genny by generating your voiceover and seamlessly syncing it with your video. Then, before exporting your video, add all the finishing touches for a truly professional look, such as subtitles, images, logos, and video clips.

Create with ease and speed

Genny is designed to allow anyone to get started immediately - no downloading software or complicated onboarding or learning is required. Simply sign in with your web browser and you are good to go! Our intuitive and easy-to-use UI makes it a breeze for anyone who needs to create content up and running in minutes. This means you can focus on what matters most - engaging and delivering your message to your audience.

AI Voice generator use cases

Corporate training & education, marketing & sales, product demos & explainers, generate voices in over 100+ languages.

Genny supports Text to Speech in:

• United States 🇺🇸
• United Kingdom 🇬🇧
• Ethiopia 🇪🇹
• Philippines 🇵🇭
• United Arab Emirates 🇦🇪
• Pakistan 🇵🇰
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Learn More About AI Voice Generators

Why do you need an ai voice generator for your videos, are ai voices ethical, how can ai voices help your business, what is the best ai voice generator, how do you generate an ai voiceover, are content generated with ai voices copyrighted, can a voice generator produce different accents or languages, what industries benefit most from ai voice technology, is the speech from a voice generator realistic, how can i customize a voice generator to fit my needs, what future developments are expected in ai voice technology, where can i find a voice generator for free.

Text to Speech

Speech to text, vocal remover, voice enhancer, audio cutter, audio joiner.

Text to speech mp3 in natural voices. Free for commercial.

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Free English Text to Speech & AI Voice Generator

How to create english text to speech, find a voice, select the model, enter text & adjust settings, generate audio.

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Contextual awareness, natural pauses, library of hq voices, customizable accents, tone and emotional control, english ai voice applications, storytelling and audiobooks, marketing and branding, educational content, voice assistants and ivr, hear from our text to speech users.

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It's amazing to see that text to speech became that good. Write your text, select a voice and receive stunning and near-perfect results! Regenerating results will also give you different results (depending on the settings). The service supports 30+ languages, including Dutch (which is very rare). ElevenLabs has proved that it isn't impossible to have near-perfect text-to-speech 'Dutch'...

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I like that ElevenLabs uses cutting-edge AI and deep learning to create incredibly natural-sounding speech synthesis and text-to-speech. The voices generated are lifelike and emotive.

English AI Voice Generator

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Eleven Multilingual offers more than a basic text-to-speech service. It uses advanced AI and deep learning to create clear, emotionally engaging speech. It doesn't just translate words; it also captures the subtle aspects of language, like local accents and cultural context, making your content more relatable to a wide range of audiences.

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Our pricing is based on the number of characters you generate. You can generate 10,000 characters for free every month. Find out more in our pricing page.

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Text to speech (TTS) is a technology that converts text into spoken audio. It's used to create voiceovers for a variety of content, including videos, audiobooks, and podcasts.

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ElevenLabs offers the best English text to speech (TTS) online. Our AI-powered technology ensures clear, high-quality audio that's engaging and relatable. We are rated 4.8/5 on G2 and have millions of happy customers.

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Voicemod AI Text To Song Generator v1.0

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The Ultimate Guide to Text to Voice Download Tools: Why Speechify Text-to-Speech is the Top Choice

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Table of contents, understanding text-to-voice downloaders, versatile applications of text to speech tools, why choose speechify text-to-speech.

Allowing users to download audio files of text transcribed into speech, TTS downloaders are becoming more and more popular. Read on to learn more.

In an increasingly digitized world, text to voice download tools have become essential tools for a wide range of applications. Whether you're looking to convert written content into audio files for audiobooks, podcasts, language learning, or enhancing accessibility, text-to-speech (TTS) technology offers the perfect solution. This article explores the world of TTS downloaders, their diverse applications, and why Speechify Text-to-Speech stands out as the top choice for users seeking high-quality, versatile, and user-friendly TTS conversion. Text-to-speech tools are invaluable for transforming written content into spoken words, making information more accessible and engaging. These tools offer a wide array of languages, including English, and utilize advanced AI voice technology to generate natural-sounding voices. From French to Hindi, Spanish to Chinese, and many more, text-to-speech tools support a multitude of languages, ensuring content is accessible to a global audience. Users can convert text into various formats, such as MP3 files or WAV, and even choose between human-like voices or AI-generated speech voices to suit their preferences. Whether for educational, entertainment, or accessibility purposes, these tools play a vital role in bridging language barriers and enhancing the overall user experience.

Text-to-voice downloaders are software or online services that convert written text into audio files. They utilize advanced speech synthesis technologies, such as AI-driven voice generators, to create natural-sounding voices that mimic human speech. These often free text to speech tools enable users to access content in an auditory format, making it easier to absorb information on the go or assist individuals with visual impairments.

Text-to-voice API downloaders serve a multitude of purposes across various domains:

• Audiobooks: TTS technology allows for the conversion of books, articles, or any written content into audiobooks, making literature accessible to those with busy schedules or visual impairments.
• Podcasts: Content creators can leverage TTS downloaders to automate the creation of podcast scripts or turn written articles into audio episodes.
• Language Learning: TTS tools facilitate language learning by pronouncing words and phrases in multiple languages, improving pronunciation and fluency.
• Accessibility: TTS is a critical tool for individuals with visual impairments, ensuring they have equal access to written content.
• E-learning: TTS technology enhances online learning experiences by providing audio versions of educational materials, making them more engaging and accessible.
• Content Creation: Content creators use TTS to generate voiceovers for video content, YouTube videos, and social media clips, saving time and resources.

Speechify text-to-speech is a versatile and powerful tool that caters to a wide range of linguistic needs. Offering human voice support for numerous languages, including German, Polish, Vietnamese, Dutch, Czech, Danish, Greek, Italian, Japanese, Romanian, Turkish, and Korean, Speechify empowers users to convert online text into spoken content seamlessly. Whether you're looking for human-like voices or AI-generated speech, Speechify provides a variety of options to choose from, allowing you to customize your listening experience. With a user-friendly interface and compatibility with languages like Portuguese, Russian, and Arabic, Speechify ensures that its service is accessible and effective for users around the world. Whether you're seeking to enhance your educational experience or make AI text content more accessible, Speechify offers a comprehensive solution that meets your needs and surpasses language barriers. Speechify Text-to-Speech stands out as the top choice among TTS downloaders for several compelling reasons:

• High-Quality Voices: Speechify offers a wide selection of natural-sounding voices in multiple languages, ensuring that your audio files are clear and pleasant to listen to.
• User-Friendly Interface: With its intuitive interface and easy-to-navigate features, Speechify is accessible to both beginners and experienced users.
• Versatility: Whether you're converting written content to audio, creating audiobooks, or generating voiceovers for videos, Speechify's versatility meets your needs.
• Online and Offline Access: Speechify works seamlessly both online and offline, allowing you to convert text to voice wherever and whenever you need it.
• Customizable Voice Settings: Users can customize voice settings to control pitch, speed, and other parameters, ensuring that the generated audio aligns with their preferences.
• Integration: Speechify integrates seamlessly with popular platforms like Amazon Kindle, making it easy to convert e-books into audiobooks.
• Cross-Platform Compatibility: Speechify is available on various platforms, including Windows, Mac, iOS, and Android, ensuring that users can access the tool on their preferred device.
• Commercial Use: Speechify offers commercial licenses for businesses and content creators, allowing them to use TTS technology for professional applications.
• SSML Support: Advanced users can take advantage of Speech Synthesis Markup Language (SSML) to fine-tune the prosody and pronunciation of the generated speech.
• Excellent Customer Support: Speechify provides responsive customer support to assist users with any questions or issues they may encounter.

In conclusion, text-to-voice downloaders like Speechify Text-to-Speech have become indispensable tools for various applications, from creating audiobooks to enhancing accessibility. Among the numerous TTS options available, Speechify stands out as the top choice due to its high-quality voices, user-friendly interface, versatility, and cross-platform compatibility. Whether you're an audiobook enthusiast, content creator, or someone seeking accessible content, Speechify offers a comprehensive and reliable solution for all your text-to-speech needs.

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Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

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• Published: 13 May 2024

Representation of internal speech by single neurons in human supramarginal gyrus

• Sarah K. Wandelt   ORCID: orcid.org/0000-0001-9551-8491 1 , 2 ,
• David A. Bjånes 1 , 2 , 3 ,
• Kelsie Pejsa 1 , 2 ,
• Brian Lee 1 , 4 , 5 ,
• Charles Liu   ORCID: orcid.org/0000-0001-6423-8577 1 , 3 , 4 , 5 &
• Richard A. Andersen 1 , 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Brain–machine interface
• Neural decoding

Speech brain–machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.

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Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS

A high-performance speech neuroprosthesis

The speech neuroprosthesis

Speech is one of the most basic forms of human communication, a natural and intuitive way for humans to express their thoughts and desires. Neurological diseases like amyotrophic lateral sclerosis (ALS) and brain lesions can lead to the loss of this ability. In the most severe cases, patients who experience full-body paralysis might be left without any means of communication. Patients with ALS self-report loss of speech as their most serious concern 1 . Brain–machine interfaces (BMIs) are devices offering a promising technological path to bypass neurological impairment by recording neural activity directly from the cortex. Cognitive BMIs have demonstrated potential to restore independence to participants with tetraplegia by reading out movement intent directly from the brain 2 , 3 , 4 , 5 . Similarly, reading out internal (also reported as inner, imagined or covert) speech signals could allow the restoration of communication to people who have lost it.

Decoding speech signals directly from the brain presents its own unique challenges. While non-invasive recording methods such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG) or magnetoencephalography 6 are important tools to locate speech and internal speech production, they lack the necessary temporal and spatial resolution, adequate signal-to-noise ratio or portability for building an online speech BMI 7 , 8 , 9 . For example, state-of-the-art EEG-based imagined speech decoding performances in 2022 ranged from approximately 60% to 80% binary classification 10 . Intracortical electrophysiological recordings have higher signal-to-noise ratios and excellent temporal resolution 11 and are a more suitable choice for an internal speech decoding device.

Invasive speech decoding has predominantly been attempted with electrocorticography (ECoG) 9 or stereo-electroencephalographic depth arrays 12 , as they allow sampling neural activity from different parts of the brain simultaneously. Impressive results in vocalized and attempted speech decoding and reconstruction have been achieved using these techniques 13 , 14 , 15 , 16 , 17 , 18 . However, vocalized speech has also been decoded from localized regions of the cortex. In 2009, the use of a neurotrophic electrode 19 demonstrated real-time speech synthesis from the motor cortex. More recently, speech neuroprosthetics were built from small-scale microelectrode arrays located in the motor cortex 20 , 21 , premotor cortex 22 and supramarginal gyrus (SMG) 23 , demonstrating that vocalized speech BMIs can be built using neural signals from localized regions of cortex.

While important advances in vocalized speech 16 , attempted speech 18 and mimed speech 17 , 22 , 24 , 25 , 26 decoding have been made, highly accurate internal speech decoding has not been achieved. Lack of behavioural output, lower signal-to-noise ratio and differences in cortical activations compared with vocalized speech are speculated to contribute to lower classification accuracies of internal speech 7 , 8 , 13 , 27 , 28 . In ref. 29 , patients implanted with ECoG grids over frontal, parietal and temporal regions silently read or vocalized written words from a screen. They significantly decoded vowels (37.5%) and consonants (36.3%) from internal speech (chance level 25%). Ikeda et al. 30 decoded three internally spoken vowels using ECoG arrays using frequencies in the beta band, with up to 55.6% accuracy from the Broca area (chance level 33%). Using the same recording technology, ref. 31 investigated the decoding of six words during internal speech. The authors demonstrated an average pair-wise classification accuracy of 58%, reaching 88% for the highest pair (chance level 50%). These studies were so-called open-loop experiments, in which the data were analysed offline after acquisition. A recent paper demonstrated real-time (closed-loop) speech decoding using stereotactic depth electrodes 32 . The results were encouraging as internal speech could be detected; however, the reconstructed audio was not discernable and required audible speech to train the decoding model.

While, to our knowledge, internal speech has not previously been decoded from SMG, evidence for internal speech representation in the SMG exists. A review of 100 fMRI studies 33 not only described SMG activity during speech production but also suggested its involvement in subvocal speech 34 , 35 . Similarly, an ECoG study identified high-frequency SMG modulation during vocalized and internal speech 36 . Additionally, fMRI studies have demonstrated SMG involvement in phonologic processing, for instance, during tasks while participants reported whether two words rhyme 37 . Performing such tasks requires the participant to internally ‘hear’ the word, indicating potential internal speech representation 38 . Furthermore, a study performed in people suffering from aphasia found that lesions in the SMG and its adjacent white matter affected inner speech rhyming tasks 39 . Recently, ref. 16 showed that electrode grids over SMG contributed to vocalized speech decoding. Finally, vocalized grasps and colour words were decodable from SMG from one of the same participants involved in this work 23 . These studies provide evidence for the possibility of an internal speech decoder from neural activity in the SMG.

The relationship between inner speech and vocalized speech is still debated. The general consensus posits similarities between internal and vocalized speech processes 36 , but the degree of overlap is not well understood 8 , 35 , 40 , 41 , 42 . Characterizing similarities between vocalized and internal speech could provide evidence that results found with vocalized speech could translate to internal speech. However, such a relationship may not be guaranteed. For instance, some brain areas involved in vocalized speech might be poor candidates for internal speech decoding.

In this Article, two participants with tetraplegia performed internal and vocalized speech of eight words while neurophysiological responses were captured from two implant sites. To investigate neural semantic and phonetic representation, the words were composed of six lexical words and two pseudowords (words that mimic real words without semantic meaning). We examined representations of various language processes at the single-neuron level using recording microelectrode arrays from the SMG located in the posterior parietal cortex (PPC) and the arm and/or hand regions of the primary somatosensory cortex (S1). S1 served as a control for movement, due to emerging evidence of its activation beyond defined regions of interest 43 , 44 . Words were presented with an auditory or a written cue and were produced internally as well as orally. We hypothesized that SMG and S1 activity would modulate during vocalized speech and that SMG activity would modulate during internal speech. Shared representation between internal speech, vocalized speech, auditory comprehension and word reading processes was investigated.

Task design

We characterized neural representations of four different language processes within a population of SMG and S1 neurons: auditory comprehension, word reading, internal speech and vocalized speech production. In this manuscript, internal speech refers to engaging a prompted word internally (‘inner monologue’), without correlated motor output, while vocalized speech refers to audibly vocalizing a prompted word. Participants were implanted in the SMG and S1 on the basis of grasp localization fMRI tasks (Fig. 1 ).

a , b , SMG implant locations in participant 1 (1 × 96 multielectrode array) ( a ) and participant 2 (1 × 64 multielectrode array) ( b ). c , d , S1 implant locations in participant 1 (2 × 96 multielectrode arrays) ( c ) and participant 2 (2 × 64 multielectrode arrays) ( d ).

The task contained six phases: an inter-trial interval (ITI), a cue phase (cue), a first delay (D1), an internal speech phase (internal), a second delay (D2) and a vocalized speech phase (speech). Words were cued with either an auditory or a written version of the word (Fig. 2a ). Six of the words were informed by ref. 31 (battlefield, cowboy, python, spoon, swimming and telephone). Two pseudowords (nifzig and bindip) were added to explore phonetic representation in the SMG. The first participant completed ten session days, composed of both the auditory and the written cue tasks. The second participant completed nine sessions, focusing only on the written cue task. The participants were instructed to internally say the cued word during the internal speech phase and to vocalize the same word during the speech phase.

a , Written words and sounds were used to cue six words and two pseudowords in a participant with tetraplegia. The ‘audio cue’ task was composed of an ITI, a cue phase during which the sound of one of the words was emitted from a speaker (between 842 and 1,130 ms), a first delay (D1), an internal speech phase, a second delay (D2) and a vocalized speech phase. The ‘written cue’ task was identical to the ‘audio cue’ task, except that written words appeared on the screen for 1.5 s. Eight repetitions of eight words were performed per session day and per task for the first participant. For the second participant, 16 repetitions of eight words were performed for the written cue task. b – e , Example smoothed firing rates of neurons tuned to four words in the SMG for participant 1 (auditory cue, python ( b ), and written cue, telephone ( c )) and participant 2 (written cue, nifzig ( d ), and written cue, spoon ( e )). Top: the average firing rate over 8 or 16 trials (solid line, mean; shaded area, 95% bootstrapped confidence interval). Bottom: one example trial with associated audio amplitude (grey). Vertically dashed lines indicate the beginning of each phase. Single neurons modulate firing rate during internal speech in the SMG.

For each of the four language processes, we observed selective modulation of individual neurons’ firing rates (Fig. 2b–e ). In general, the firing rates of neurons increased during the active phases (cue, internal and speech) and decreased during the rest phases (ITI, D1 and D2). A variety of activation patterns were present in the neural population. Example neurons were selected to demonstrate increases in firing rates during internal speech, cue and vocalized speech. Both the auditory (Fig. 2b ) and the written cue (Fig. 2c–e ) evoked highly modulated firing rates of individual neurons during internal speech.

These stereotypical activation patterns were evident at the single-trial level (Fig. 2b–e , bottom). When the auditory recording was overlaid with firing rates from a single trial, a heterogeneous neural response was observed (Supplementary Fig. 1a ), with some SMG neurons preceding or lagging peak auditory levels during vocalized speech. In contrast, neural activity from primary sensory cortex (S1) only modulated during vocalized speech and produced similar firing patterns regardless of the vocalized word (Supplementary Fig. 1b ).

Population activity represented selective tuning for individual words

Population analysis in the SMG mirrored single-neuron patterns of activation, showing increases in tuning during the active task phases (Fig. 3a,d ). Tuning of a neuron to a word was determined by fitting a linear regression model to the firing rate in 50-ms time bins ( Methods ). Distinctions between participant 1 and participant 2 were observed. Specifically, participant 1 exhibited strong tuning, whereas the number of tuned units was notably lower in participant 2. Based on these findings, we exclusively ran the written cue task with participant number 2. In participant 1, representation of the auditory cue was lower compared with the written cue (Fig. 3b , cue). However, this difference was not observed for other task phases. In both participants, the tuned population activity in S1 increased during vocalized speech but not during the cue and internal speech phases (Supplementary Fig. 3a,b ).

a , The average percentage of tuned neurons to words in 50-ms time bins in the SMG over the trial duration for ‘auditory cue’ (blue) and ‘written cue’ (green) tasks for participant 1 (solid line, mean over ten sessions; shaded area, 95% confidence interval of the mean). During the cue phase of auditory trials, neural data were aligned to audio onset, which occurred within 200–650 ms following initiation of the cue phase. b , The average percentage of tuned neurons computed on firing rates per task phase, with 95% confidence interval over ten sessions. Tuning during action phases (cue, internal and speech) following rest phases (ITI, D1 and D2) was significantly higher (paired two-tailed t -test, d.f. 9, P ITI_CueWritten  < 0.001, Cohen’s d  = 2.31; P ITI_CueAuditory  = 0.003, Cohen’s d  = 1.25; P D1_InternalWritten  = 0.008, Cohen’s d  = 1.08; P D1_InternalAuditory  < 0.001, Cohen’s d  = 1.71; P D2_SpeechWritten  < 0.001, Cohen’s d  = 2.34; P D2_SpeechAuditory  < 0.001, Cohen’s d  = 3.23). c , The number of neurons tuned to each individual word in each phase for the ‘auditory cue’ and ‘written cue’ tasks. d , The average percentage of tuned neurons to words in 50-ms time bins in the SMG over the trial duration for ‘written cue’ (green) tasks for participant 2 (solid line, mean over nine sessions; shaded area, 95% confidence interval of the mean). Due to a reduced number of tuned units, only the ‘written cue’ task variation was performed. e , The average percentage of tuned neurons computed on firing rates per task phase, with 95% confidence interval over nine sessions. Tuning during cue and internal phases following rest phases ITI and D1 was significantly higher (paired two-tailed t -test, d.f. 8, P ITI_CueWritten  = 0.003, Cohen’s d  = 1.38; P D1_Internal  = 0.001, Cohen’s d  = 1.67). f , The number of neurons tuned to each individual word in each phase for the ‘written cue’ task.

Source data

To quantitatively compare activity between phases, we assessed the differential response patterns for individual words by examining the variations in average firing rate across different task phases (Fig. 3b,e ). In both participants, tuning during the cue and internal speech phases was significantly higher compared with their preceding rest phases ITI and D1 (paired t -test between phases. Participant 1: d.f. 9, P ITI_CueWritten  < 0.001, Cohen’s d  = 2.31; P ITI_CueAuditory  = 0.003, Cohen’s d  = 1.25; P D1_InternalWritten  = 0.008, Cohen’s d  = 1.08; P D1_InternalAuditory  < 0.001, Cohen’s d  = 1.71. Participant 2: d.f. 8, P ITI_CueWritten  = 0.003, Cohen’s d  = 1.38; P D1_Internal  = 0.001, Cohen’s d  = 1.67). For participant 1, we also observed significantly higher tuning to vocalized speech than to tuning in D2 (d.f. 9, P D2_SpeechWritten  < 0.001, Cohen’s d  = 2.34; P D2_SpeechAuditory  < 0.001, Cohen’s d  = 3.23). Representation for all words was observed in each phase, including pseudowords (bindip and nifzig) (Fig. 3c,f ). To identify neurons with selective activity for unique words, we performed a Kruskal–Wallis test (Supplementary Fig. 3c,d ). The results mirrored findings of the regression analysis in both participants, albeit weaker in participant 2. These findings suggest that, while neural activity during active phases differed from activity during the ITI phase, neural responses of only a few neurons varied across different words for participant 2.

The neural population in the SMG simultaneously represented several distinct aspects of language processing: temporal changes, input modality (auditory, written for participant 1) and unique words from our vocabulary list. We used demixed principal component analysis (dPCA) to decompose and analyse contributions of each individual component: timing, cue modality and word. In Fig. 4 , demixed principal components (PCs) explaining the highest amount of variance were plotted by projecting data onto their respective dPCA decoder axis.

a – e , dPCA was performed to investigate variance within three marginalizations: ‘timing’, ‘cue modality’ and ‘word’ for participant 1 ( a – c ) and ‘timing’ and ‘word’ for participant 2 ( d and e ). Demixed PCs explaining the highest variance within each marginalization were plotted over time, by projecting the data onto their respective dPCA decoder axis. In a , the ‘timing’ marginalization demonstrates SMG modulation during cue, internal speech and vocalized speech, while S1 only represents vocalized speech. The solid blue lines (8) represent the auditory cue trials, and dashed green lines (8) represent written cue trials. In b , the ‘cue modality’ marginalization suggests that internal and vocalized speech representation in the SMG are not affected by the cue modality. The solid blue lines (8) represent the auditory cue trials, and dashed green lines (8) represent written cue trials. In c , the ‘word’ marginalization shows high variability for different words in the SMG, but near zero for S1. The colours (8) represent individual words. For each colour, solid lines represent auditory trials and dashed lines represent written cue trials. d is the same as a , but for participant 2. The dashed green lines (8) represent written cue trials. e is the same as c , but for participant 2. The colours (8) represent individual words during written cue trials. The variance for different words in the SMG (left) was higher than in S1 (right), but lower in comparison with SMG in participant 1 ( c ).

For participant 1, the ‘timing’ component revealed that temporal dynamics in the SMG peaked during all active phases (Fig. 4a ). In contrast, temporal S1 modulation peaked only during vocalized speech production, indicating a lack of synchronized lip and face movement of the participant during the other task phases. While ‘cue modality’ components were separable during the cue phase (Fig. 4b ), they overlapped during subsequent phases. Thus, internal and vocalized speech representation may not be influenced by the cue modality. Pseudowords had similar separability to lexical words (Fig. 4c ). The explained variance between words was high in the SMG and was close to zero in S1. In participant 2, temporal dynamics of the task were preserved (‘timing’ component). However, variance to words was reduced, suggesting lower neuronal ability to represent individual words in participant 2. In S1, the results mirrored findings from S1 in participant 1 (Fig. 4d,e , right).

Internal speech is decodable in the SMG

Separable neural representations of both internal and vocalized speech processes implicate SMG as a rich source of neural activity for real-time speech BMI devices. The decodability of words correlated with the percentage of tuned neurons (Fig. 3a–f ) as well as the explained dPCA variance (Fig. 4c,e ) observed in the participants. In participant 1, all words in our vocabulary list were highly decodable, averaging 55% offline decoding and 79% (16–20 training trials) online decoding from neurons during internal speech (Fig. 5a,b ). Words spoken during the vocalized phase were also highly discriminable, averaging 74% offline (Fig. 5a ). In participant 2, offline internal speech decoding averaged 24% (Supplementary Fig. 4b ) and online decoding averaged 23% (Fig. 5a ), with preferential representation of words ‘spoon’ and ‘swimming’.

a , Offline decoding accuracies: ‘audio cue’ and ‘written cue’ task data were combined for each individual session day, and leave-one-out CV was performed (black dots). PCA was performed on the training data, an LDA model was constructed, and classification accuracies were plotted with 95% confidence intervals, over the session means. The significance of classification accuracies were evaluated by comparing results with a shuffled distribution (averaged shuffle results over 100 repetitions indicated by red dots; P  < 0.01 indicates that the average mean is >99.5th percentile of shuffle distribution, n  = 10). In participant 1, classification accuracies during action phases (cue, internal and speech) following rest phases (ITI, D1 and D2) were significantly higher (paired two-tailed t -test: n  = 10, d.f. 9, for all P  < 0.001, Cohen’s d  = 6.81, 2.29 and 5.75). b , Online decoding accuracies: classification accuracies for internal speech were evaluated in a closed-loop internal speech BMI application on three different session days for both participants. In participant 1, decoding accuracies were significantly above chance (averaged shuffle results over 1,000 repetitions indicated by red dots; P  < 0.001 indicates that the average mean is >99.95th percentile of shuffle distribution) and improved when 16–20 trials per words were used to train the model (two-sample two-tailed t -test, n (8–14)  = 8, d.f. 11, n (16–20)  = 5, P  = 0.029), averaging 79% classification accuracy. In participant 2, online decoding accuracies were significant (averaged shuffle results over 1,000 repetitions indicated by red dots; P  < 0.05 indicates that average mean is >97.5th percentile of shuffle distribution, n  = 7) and averaged 23%. c , An offline confusion matrix for participant 1: confusion matrices for each of the different task phases were computed on the tested data and averaged over all session days. d , An online confusion matrix: a confusion matrix was computed combining all online runs, leading to a total of 304 trials (38 trials per word) for participant 1 and 448 online trials for participant 2. Participant 1 displayed comparable online decoding accuracies for all words, while participant 2 had preferential decoding for the words ‘swimming’ and ‘spoon’.

In participant 1, trial data from both types of cue (auditory and written) were concatenated for offline analysis, since SMG activity was only differentiable between the types of cue during the cue phase (Figs. 3a and 4b ). This resulted in 16 trials per condition. Features were selected via principal component analysis (PCA) on the training dataset, and PCs that explained 95% of the variance were kept. A linear discriminant analysis (LDA) model was evaluated with leave-one-out cross-validation (CV). Significance was computed by comparing results with a null distribution ( Methods ).

Significant word decoding was observed during all phases, except during the ITI (Fig. 5a , n  = 10, mean decoding value above 99.5th percentile of shuffle distribution is P  < 0.01, per phase, Cohen’s d  = 0.64, 6.17, 3.04, 6.59, 3.93 and 8.26, confidence interval of the mean ± 1.73, 4.46, 5.21, 5.67, 4.63 and 6.49). Decoding accuracies were significantly higher in the cue, internal speech and speech condition, compared with rest phases ITI, D1 and D2 (Fig. 5a , paired t -test, n  = 10, d.f. 9, for all P  < 0.001, Cohen’s d  = 6.81, 2.29 and 5.75). Significant cue phase decoding suggested that modality-independent linguistic representations were present early within the task 45 . Internal speech decoding averaged 55% offline, with the highest session at 72% and a chance level of ~12.5% (Fig. 5a , red line). Vocalized speech averaged even higher, at 74%. All words were highly decodable (Fig. 5c ). As suggested from our dPCA results, individual words were not significantly decodable from neural activity in S1 (Supplementary Fig. 4a ), indicating generalized activity for vocalized speech in the S1 arm region (Fig. 4c ).

For participant 2, SMG significant word decoding was observed during the cue, internal and vocalized speech phases (Supplementary Fig. 4b , n  = 9, mean decoding value above 97.5th/99.5th percentile of shuffle distribution is P  < 0.05/ P  < 0.01, per phase Cohen’s d  = 0.35, 1.15, 1.09, 1.44, 0.99 and 1.49, confidence interval of the mean ± 3.09, 5.02, 6.91, 8.14, 5.45 and 4.15). Decoding accuracies were significantly higher in the cue and internal speech condition, compared with rest phases ITI and D1 (Supplementary Fig. 4b , paired t -test, n  = 9, d.f. 8, P ITI_Cue  = 0.013, Cohen’s d  = 1.07, P D1_Internal  = 0.01, Cohen’s d  = 1.11). S1 decoding mirrored results in participant 1, suggesting that no synchronized face movements occurred during the cue phase or internal speech phase (Supplementary Fig. 4c ).

High-accuracy online speech decoder

We developed an online, closed-loop internal speech BMI using an eight-word vocabulary (Fig. 5b ). On three separate session days, training datasets were generated using the written cue task, with eight repetitions of each word for each participant. An LDA model was trained on the internal speech data of the training set, corresponding to only 1.5 s of neural data per repetition for each class. The trained decoder predicted internal speech during the online task. During the online task, the vocalized speech phase was replaced with a feedback phase. The decoded word was shown in green if correctly decoded, and in red if wrongly decoded (Supplementary Video 1 ). The classifier was retrained after each run of the online task, adding the newly recorded data. Several online runs were performed on each session day, corresponding to different datapoints on Fig. 5b . When using between 8 and 14 repetitions per words to train the decoding model, an average of 59% classification accuracy was obtained for participant 1. Accuracies were significantly higher (two-sample two-tailed t -test, n (8–14)  = 8, n (16–20)  = 5, d.f. 11, P  = 0.029) the more data were added to train the model, obtaining an average of 79% classification accuracy with 16–20 repetitions per word. The highest single run accuracy was 91%. All words were well represented, illustrated by a confusion matrix of 304 trials (Fig. 5d ). In participant 2, decoding was statistically significant, but lower compared with participant 1. The lower number of tuned units (Fig. 3a–f ) and reduced explained variance between words (Fig. 4e , left) could account for these findings. Additionally, preferential representation of words ‘spoon’ and ‘swimming’ was observed.

Shared representations between internal speech, written words and vocalized speech

Different language processes are engaged during the task: auditory comprehension or visual word recognition during the cue phase, and internal speech and vocalized speech production during the speech phases. It has been widely assumed that each of these processes is part of a highly distributed network, involving multiple cortical areas 46 . In this work, we observed significant representation of different language processes in a common cortical region, SMG, in our participants. To explore the relationships between each of these processes, for participant 1 we used cross-phase classification to identify the distinct and common neural codes separately in the auditory and written cue datasets. By training our classifier on the representation found in one phase (for example, the cue phase) and testing the classifier on another phase (for example, internal speech), we quantified generalizability of our models across neural activity of different language processes (Fig. 6 ). The generalizability of a model to different task phases was evaluated through paired t -tests. No significant difference between classification accuracies indicates good generalization of the model, while significantly lower classification accuracies suggest poor generalization of the model.

a , Evaluating the overlap of shared information between different task phases in the ‘auditory cue’ task. For each of the ten session days, cross-phase classification was performed. It consisted in training a model on a subset of data from one phase (for example, cue) and applying it on a subset of data from ITI, cue, internal and speech phases. This analysis was performed separately for each task phase. PCA was performed on the training data, an LDA model was constructed and classification accuracies were plotted with a 95% confidence interval over session means. Significant differences in performance between phases were evaluated between the ten sessions (paired two-tailed t -test, FDR corrected, d.f. 9, P  < 0.001 for all, Cohen’s d  ≥ 1.89). For easier visibility, significant differences between ITI and other phases were not plotted. b , Same as a for the ‘written cue’ task (paired two-tailed t -test, FDR corrected, d.f. 9, P Cue_Internal  = 0.028, Cohen’s d  > 0.86; P Cue_Speech  = 0.022, Cohen’s d  = 0.95; all others P  < 0.001 and Cohen’s d  ≥ 1.65). c , The percentage of neurons tuned during the internal speech phase that are also tuned during the vocalized speech phase. Neurons tuned during the internal speech phase were computed as in Fig. 3b separately for each session day. From these, the percentage of neurons that were also tuned during vocalized speech was calculated. More than 80% of neurons during internal speech were also tuned during vocalized speech (82% in the ‘auditory cue’ task, 85% in the ‘written cue’ task). In total, 71% of ‘auditory cue’ and 79% ‘written cue’ neurons also preserved tuning to at least one identical word during internal speech and vocalized speech phases. d , The percentage of neurons tuned during the internal speech phase that were also tuned during the cue phase. Right: 78% of neurons tuned during internal speech were also tuned during the written cue phase. Left: a smaller 47% of neurons tuned during the internal speech phase were also tuned during the auditory cue phase. In total, 71% of neurons preserved tuning between the written cue phase and the internal speech phase, while 42% of neurons preserved tuning between the auditory cue and the internal speech phase.

The strongest shared neural representations were found between visual word recognition, internal speech and vocalized speech (Fig. 6b ). A model trained on internal speech was highly generalizable to both vocalized speech and written cued words, evidence for a possible shared neural code (Fig. 6b , internal). In contrast, the model’s performance was significantly lower when tested on data recorded in the auditory cue phase (Fig. 6a , training phase internal: paired t -test, d.f. 9, P Cue_Internal  < 0.001, Cohen’s d  = 2.16; P Cue_Speech  < 0.001, Cohen’s d  = 3.34). These differences could stem from the inherent challenges in comparing visual and auditory language stimuli, which differ in processing time: instantaneous for text versus several hundred milliseconds for auditory stimuli.

We evaluated the capability of a classification model, initially trained to distinguish words during vocalized speech, in its ability to generalize to internal and cue phases (Fig. 6a,b , training phase speech). The model demonstrated similar levels of generalization during internal speech and in response to written cues, as indicated by the lack of significance in decoding accuracy between the internal and written cue phase (Fig. 6b , training phase speech, cue–internal). However, the model generalized significantly better to internal speech than to representations observed during the auditory cue phase (Fig. 6a , training phase speech, d.f. 9, P Cue_Internal  < 0.001, Cohen’s d  = 2.85).

Neuronal representation of words at the single-neuron level was highly consistent between internal speech, vocalized speech and written cue phases. A high percentage of neurons were not only active during the same task phases but also preserved identical tuning to at least one word (Fig. 6c,d ). In total, 82–85% of neurons active during internal speech were also active during vocalized speech. In 71–79% of neurons, tuning was preserved between the internal speech and vocalized speech phases (Fig. 6c ). During the cue phase, 78% of neurons active during internal speech were also active during the written cue (Fig. 6d , right). However, a lower percentage of neurons (47%) were active during the auditory cue phase (Fig. 6d , left). Similarly, 71% of neurons preserved tuning between the written cue phase and the internal speech phase, while 42% of neurons preserved tuning between the auditory cue phase and the internal speech phase.

Together with the cross-phase analysis, these results suggest strong shared neural representations between internal speech, vocalized speech and the written cue, both at the single-neuron and at the population level.

Robust decoding of multiple internal speech strategies within the SMG

Strong shared neural representations in participant 1 between written, inner and vocalized speech suggest that all three partly represent the same cognitive process or all cognitive processes share common neural features. While internal and vocalized speech have been shown to share common neural features 36 , similarities between internal speech and the written cue could have occurred through several different cognitive processes. For instance, the participant’s observation of the written cue could have activated silent reading. This process has been self-reported as activating internal speech, which can involve ‘hearing’ a voice, thus having an auditory component 42 , 47 . However, the participant could also have mentally pictured an image of the written word while performing internal speech, involving visual imagination in addition to language processes. Both hypotheses could explain the high amount of shared neural representation between the written cue and the internal speech phases (Fig. 6b ).

We therefore compared two possible internal sensory strategies in participant 1: a ‘sound imagination’ strategy in which the participant imagined hearing the word, and a ‘visual imagination’ strategy in which the participant visualized the word’s image (Supplementary Fig. 5a ). Each strategy was cued by the modalities we had previously tested (auditory and written words) (Table 1 ). To assess the similarity of these internal speech processes to other task phases, we conducted a cross-phase decoding analysis (as performed in Fig. 6 ). We hypothesized that, if the high cross-decoding results between internal and written cue phases primarily stemmed from the participant engaging in visual word imagination, we would observe lower decoding accuracies during the auditory imagination phase.

Both strategies demonstrated high representation of the four-word dataset (Supplementary Fig. 5b , highest 94%, chance level 25%). These results suggest our speech BMI decoder is robust to multiple types of internal speech strategy.

The participant described the ‘sound imagination’ strategy as being easier and more similar to the internal speech condition of the first experiment. The participant’s self-reported strategy suggests that no visual imagination was performed during internal speech. Correspondingly, similarities between written cue and internal speech phases may stem from internal speech activation during the silent reading of the cue.

In this work, we demonstrated a decoder for internal and vocalized speech, using single-neuron activity from the SMG. Two chronically implanted, speech-abled participants with tetraplegia were able to use an online, closed-loop internal speech BMI to achieve on average 79% and 23% classification accuracy with 16–32 training trials for an eight-word vocabulary. Furthermore, high decoding was achievable with only 24 s of training data per word, corresponding to 16 trials each with 1.5 s of data. Firing rates recorded from S1 showed generalized activation only during vocalized speech activity, but individual words were not classifiable. In the SMG, shared neural representations between internal speech, the written cue and vocalized speech suggest the occurrence of common processes. Robust control could be achieved using visual and auditory internal speech strategies. Representation of pseudowords provided evidence for a phonetic word encoding component in the SMG.

Single neurons in the SMG encode internal speech

We demonstrated internal speech decoding of six different words and two pseudowords in the SMG. Single neurons increased their firing rates during internal speech (Fig. 2 , S1 and S2), which was also reflected at the population level (Fig. 3a,b,d,e ). Each word was represented in the neuronal population (Fig. 3c,f ). Classification accuracy and tuning during the internal speech phase were significantly higher than during the previous delay phase (Figs. 3b,e and 5a , and Supplementary Figs. 3c,d and 4b ). This evidence suggests that we did not simply decode sustained activity from the cue phase but activity generated by the participant performing internal speech. We obtained significant offline and online internal speech decoding results in two participants (Fig. 5a and Supplementary Fig. 4b ). These findings provide strong evidence for internal speech processing at the single-neuron level in the SMG.

Neurons in S1 are modulated by vocalized but not internal speech

Neural activity recorded from S1 served as a control for synchronized face and lip movements during internal speech. While vocalized speech robustly activated sensory neurons, no increase of baseline activity was observed during the internal speech phase or the auditory and written cue phases in both participants (Fig. 4 , S1). These results underline no synchronized movement inflated our decoding accuracy of internal speech (Supplementary Fig. 4a,c ).

A previous imaging study achieved significant offline decoding of several different internal speech sentences performed by patients with mild ALS 6 . Together with our findings, these results suggest that a BMI speech decoder that does not rely on any movement may translate to communication opportunities for patients suffering from ALS and locked-in syndrome.

Different face activities are observable but not decodable in arm area of S1

The topographic representation of body parts in S1 has recently been found to be less rigid than previously thought. Generalized finger representation was found in a presumably S1 arm region of interest (ROI) 44 . Furthermore, an fMRI paper found observable face and lip activity in S1 leg and hand ROIs. However, differentiation between two lip actions was restricted to the face ROI 43 . Correspondingly, we observed generalized face and lip activity in a predominantly S1 arm region for participant 1 (see ref. 48 for implant location) and a predominantly S1 hand region for participant 2 during vocalized speech (Fig. 4a,d and Supplementary Figs. 1 and 4a,b ). Recorded neural activity contained similar representations for different spoke words (Fig. 4c,e ) and was not significantly decodable (Supplementary Fig. 4a,c ).

Shared neural representations between internal and vocalized speech

The extent to which internal and vocalized speech generalize is still debated 35 , 42 , 49 and depends on the investigated brain area 36 , 50 . In this work, we found on average stronger representation for vocalized (74%) than internal speech (Fig. 5a , 55%) in participant 1 but the opposite effect in participant 2 (Supplementary Fig. 4b , 24% internal, 21% vocalized speech). Additionally, cross-phase decoding of vocalized speech from models trained on data during internal speech resulted in comparable classification accuracies to those of internal speech (Fig. 6a,b , internal). Most neurons tuned during internal speech were also tuned to at least one of the same words during vocalized speech (71–79%; Fig. 6c ). However, some neurons were only tuned during internal speech, or to different words. These observations also applied to firing rates of individual neurons. Here, we observed neurons that had higher peak rates during the internal speech phase than the vocalized speech phase (Supplementary Fig. 1 : swimming and cowboy). Together, these results further suggest neural signatures during internal and vocalized speech are similar but distinct from one another, emphasizing the need for developing speech models from data recorded directly on internal speech production 51 .

Similar observations were made when comparing internal speech processes with visual word processes. In total, 79% of neurons were active both in the internal speech phase and the written cue phase, and 79% preserved the same tuning (Fig. 6d , written cue). Additionally, high cross-decoding between both phases was observed (Fig. 6b , internal).

Shared representation between speech and written cue presentation

Observation of a written cue may engage a variety of cognitive processes, such as visual feature recognition, semantic understanding and/or related language processes, many of which modulate similar cortical regions as speech 45 . Studies have found that silent reading can evoke internal speech; it can be modulated by a presumed author’s speaking speed, voice familiarity or regional accents 35 , 42 , 47 , 52 , 53 . During silent reading of a cued sentence with a neutral versus increased prosody (madeleine brought me versus MADELEINE brought me), one study in particular found that increased left SMG activation correlated with the intensity of the produced inner speech 54 .

Our data demonstrated high cross-phase decoding accuracies between both written cue and speech phases in our first participant (Fig. 6b ). Due to substantial shared neural representation, we hypothesize that the participant’s silent reading during the presentation of the written cue may have engaged internal speech processes. However, this same shared representation could have occurred if visual processes were activated in the internal speech phase. For instance, the participant could have performed mental visualization of the written word instead of generating an internal monologue, as the subjective perception of internal speech may vary between individuals.

Investigating internal speech strategies

In a separate experiment, participant 1 was prompted to execute different mental strategies during the internal speech phase, consisting of ‘sound imagination’ or ‘visual word imagination’ (Supplementary Fig. 5a ). We found robust decoding during the internal strategy phase, regardless of which mental strategy was performed (Supplementary Fig. 5b ). This participant reported the sound strategy was easier to execute than the visual strategy. Furthermore, this participant reported that the sound strategy was more similar to the internal speech strategy employed in prior experiments. This self-report suggests that the patient did not perform visual imagination during the internal speech task. Therefore, shared neural representation between internal and written word phases during the internal speech task may stem from silent reading of the written cue. Since multiple internal mental strategies are decodable from SMG, future patients could have flexibility with their preferred strategy. For instance, people with a strong visual imagination may prefer performing visual word imagination.

Audio contamination in decoding result

Prior studies examining neural representation of attempted or vocalized speech must potentially mitigate acoustic contamination of electrophysiological brain signals during speech production 55 . During internal speech production, no detectable audio was captured by the audio equipment or noticed by the researchers in the room. In the rare cases the participant spoke during internal speech (three trials), the trials were removed. Furthermore, if audio had contaminated the neural data during the auditory cue or vocalized speech, we would have probably observed significant decoding in all channels. However, no significant classification was detected in S1 channels during the auditory cue phase nor the vocalized speech phase (Supplementary Fig. 2b ). We therefore conclude that acoustic contamination did not artificially inflate observed classification accuracies during vocalized speech in the SMG.

Single-neuron modulation during internal speech with a second participant

We found single-neuron modulation to speech processes in a second participant (Figs. 2d,e and 3f , and Supplementary Fig. 2d ), as well as significant offline and online classification accuracies (Fig. 5a and Supplementary Fig. 4b ), confirming neural representation of language processes in the SMG. The number of neurons distinctly active for different words was lower compared with the first participant (Fig. 2e and Supplementary Fig. 3d ), limiting our ability to decode with high accuracy between words in the different task phases (Fig. 5a and Supplementary Fig. 4b ).

Previous work found that single neurons in the PPC exhibited a common neural substrate for written action verbs and observed actions 56 . Another study found that single neurons in the PPC also encoded spoken numbers 57 . These recordings were made in the superior parietal lobule whereas the SMG is in the inferior parietal lobule. Thus, it would appear that language-related activity is highly distributed across the PPC. However, the difference in strength of language representation between each participant in the SMG suggests that there is a degree of functional segregation within the SMG 37 .

Different anatomical geometries of the SMG between participants mean that precise comparisons of implanted array locations become difficult (Fig. 1 ). Implant locations for both participants were informed from pre-surgical anatomical/vasculature scans and fMRI tasks designed to evoke activity related to grasp and dexterous hand movements 48 . Furthermore, the number of electrodes of the implanted array was higher in the first participant (96) than in the second participant (64). A pre-surgical assessment of functional activity related to language and speech may be required to determine the best candidate implant locations within the SMG for online speech decoding applications.

Impact on BMI applications

In this work, an online internal speech BMI achieved significant decoding from single-neuron activity in the SMG in two participants with tetraplegia. The online decoders were trained on as few as eight repetitions of 1.5 s per word, demonstrating that meaningful classification accuracies can be obtained with only a few minutes’ worth of training data per day. This proof-of-concept suggests that the SMG may be able to represent a much larger internal vocabulary. By building models on internal speech directly, our results may translate to people who cannot vocalize speech or are completely locked in. Recently, ref. 26 demonstrated a BMI speller that decoded attempted speech of the letters of the NATO alphabet and used those to construct sentences. Scaling our vocabulary to that size could allow for an unrestricted internal speech speller.

To summarize, we demonstrate the SMG as a promising candidate to build an internal brain–machine speech device. Different internal speech strategies were decodable from the SMG, allowing patients to use the methods and languages with which they are most comfortable. We found evidence for a phonetic component during internal and vocalized speech. Adding to previous findings indicating grasp decoding in the SMG 23 , we propose the SMG as a multipurpose BMI area.

Experimental model and participant details

Two male participants with tetraplegia (33 and 39 years) were recruited for an institutional review board- and Food and Drug Administration-approved clinical trial of a BMI and gave informed consent to participate (Institutional Review Board of Rancho Los Amigos National Rehabilitation Center, Institutional Review Board of California Institute of Technology, clinical trial registration NCT01964261 ). This clinical trial evaluated BMIs in the PPC and the somatosensory cortex for grasp rehabilitation. One of the primary effectiveness objectives of the study is to evaluate the effectiveness of the neuroport in controlling virtual or physical end effectors. Signals from the PPC will allow the subjects to control the end effector with accuracy greater than chance. Participants were compensated for their participation in the study and reimbursed for any travel expenses related to participation in study activities. The authors affirm that the human research participant provided written informed consent for publication of Supplementary Video 1 . The first participant suffered a spinal cord injury at cervical level C5 1.5 years before participating in the study. The second participant suffered a C5–C6 spinal cord injury 3 years before implantation.

Method details

Data were collected from implants located in the left SMG and the left S1 (for anatomical locations, see Fig. 1 ). For description of pre-surgical planning, localization fMRI tasks, surgical techniques and methodologies, see ref. 48 . Placement of electrodes was based on fMRI tasks involving grasp and dexterous hand movements.

The first participant underwent surgery in November 2016 to implant two 96-channel platinum-tipped multi-electrode arrays (NeuroPort Array, Blackrock Microsystems) in the SMG and in the ventral premotor cortex and two 7 × 7 sputtered iridium oxide film (SIROF)-tipped microelectrode arrays with 48 channels each in the hand and arm area of S1. Data were collected between July 2021 and August 2022. The second participant underwent surgery in October 2022 and was implanted with SIROF-tipped 64-channel microelectrode arrays in S1 (two arrays), SMG, ventral premotor cortex and primary motor cortex. Data were collected in January 2023.

Data collection

Recording began 2 weeks after surgery and continued one to three times per week. Data for this work were collected between 2021 and 2023. Broadband electrical activity was recorded from the NeuroPort Arrays using Neural Signal Processors (Blackrock Microsystems). Analogue signals were amplified, bandpass filtered (0.3–7,500 Hz) and digitized at 30,000 samples s −1 . To identify putative action potentials, these broadband data were bandpass filtered (250–5,000 Hz) and thresholded at −4.5 the estimated root-mean-square voltage of the noise. For some of the analyses, waveforms captured at these threshold crossings were then spike sorted by manually assigning each observation to a putative single neuron; for others, multiunit activity was considered. For participant 1, an average of 33 sorted SMG units (between 22 and 56) and 83 sorted S1 units (between 59 and 96) were recorded per session. For participant 2, an average of 80 sorted SMG units (between 69 and 92) and 81 sorted S1 units (between 61 and 101) were recorded per session. Auditory data were recorded at 30,000 Hz simultaneously to the neural data. Background noise was reduced post-recording by using the noise reduction function of the program ‘Audible’.

Experimental tasks

We implemented different tasks to study language processes in the SMG. The tasks cued six words informed by ref. 31 (spoon, python, battlefield, cowboy, swimming and telephone) as well as two pseudowords (bindip and nifzig). The participants were situated 1 m in front of a light-emitting diode screen (1,190 mm screen diagonal), where the task was visualized. The task was implemented using the Psychophysics Toolbox 58 , 59 , 60 extension for MATLAB. Only the written cue task was used for participant 2.

Auditory cue task

Each trial consisted of six phases, referred to in this paper as ITI, cue, D1, internal, D2 and speech. The trial began with a brief ITI (2 s), followed by a 1.5-s-long cue phase. During the cue phase, a speaker emitted the sound of one of the eight words (for example, python). Word duration varied between 842 and 1,130 ms. Then, after a delay period (grey circle on screen; 0.5 s), the participant was instructed to internally say the cued word (orange circle on screen; 1.5 s). After a second delay (grey circle on screen; 0.5 s), the participant vocalized the word (green circle on screen, 1.5 s).

Written cue task

The task was identical to the auditory cue task, except words were cued in writing instead of sound. The written word appeared on the screen for 1.5 s during the cue phase. The auditory cue was played between 200 ms and 650 ms later than the written cue appeared on the screen, due to the utilization of varied sound outputs (direct computer audio versus Bluetooth speaker).

One auditory cue task and one written cue task were recorded on ten individual session days in participant 1. The written cue task was recorded on seven individual session days in participant 2.

Control experiments

Three experiments were run to investigate internal strategies and phonetic versus semantic processing.

Internal strategy task

The task was designed to vary the internal strategy employed by the participant during the internal speech phase. Two internal strategies were tested: a sound imagination and a visual imagination. For the ‘sound imagination’ strategy, the participant was instructed to imagine what the sound of the word sounded like. For the ‘visual imagination’ strategy, the participant was instructed to perform mental visualization from the written word. We also tested if the cue modality (auditory or written) influenced the internal strategy. A subset of four words were used for this experiment. This led to four different variations of the task.

The internal strategy task was run on one session day with participant 1.

Online task

The ‘written cue task’ was used for the closed-loop experiments. To obtain training data for the online task, a written cue task was run. Then, a classification model was trained only on the internal speech data of the task (see ‘Classification’ section). The closed-loop task was nearly identical to the ‘written cue task’ but replaced the vocalized speech phase by a feedback phase. Feedback was provided by showing the word on the screen either in green if correctly classified or in red if wrongly classified. See Supplementary Video 1 for an example of the participant performing the online task. The online task was run on three individual session days.

Error trials

Trials in which participants accidentally spoke during the internal speech part (3 trials) or said the wrong word during the vocalized speech part (20 trials) were removed from all analysis.

Total number of recording trials

For participant 1, we collected offline datasets composed of eight trials per word across ten sessions. Trials during which participant errors occurred were excluded. In total, between 156 and 159 trials per word were included, with a total of 1,257 trials for offline analysis. On four non-consecutive session days, the auditory cue task was run first, and on six non-consecutive days, the written cue task was run first. For online analysis, datasets were recorded on three different session days, for a total of 304 trials. Participant 2 underwent a similar data collection process, with offline datasets comprising 16 trials per word using the written cue modality over nine sessions. Error trials were excluded. In total, between 142 and 144 trials per word were kept, with a total of 1,145 trials for offline analysis. For online analysis, datasets were recorded on three session days, leading to a total of 448 online trials.

Quantification and statistical analysis

Analyses were performed using MATLAB R2020b and Python, version 3.8.11.

Neural firing rates

Firing rates of sorted units were computed as the number of spikes occurring in 50-ms bins, divided by the bin width and smoothed using a Gaussian filter with kernel width of 50 ms to form an estimate of the instantaneous firing rates (spikes s −1 ).

Linear regression tuning analysis

To identify units exhibiting selective firing rate patterns (or tuning) for each of the eight words, linear regression analysis was performed in two different ways: (1) step by step in 50-ms time bins to allow assessing changes in neuronal tuning over the entire trial duration; (2) averaging the firing rate in each task phase to compare tuning between phases. The model returns a fit that estimates the firing rate of a unit on the basis of the following variables:

where FR corresponds to the firing rate of the unit, β 0 to the offset term equal to the average ITI firing rate of the unit, X is the vector indicator variable for each word w , and β w corresponds to the estimated regression coefficient for word w . W was equal to 8 (battlefield, cowboy, python, spoon, swimming, telephone, bindip and nifzig) 23 .

In this model, β symbolizes the change of firing rate from baseline for each word. A t -statistic was calculated by dividing each β coefficient by its standard error. Tuning was based on the P value of the t -statistic for each β coefficient. A follow-up analysis was performed to adjust for false discovery rate (FDR) between the P values 61 , 62 . A unit was defined as tuned if the adjusted P value is <0.05 for at least one word. This definition allowed for tuning of a unit to zero, one or multiple words during different timepoints of the trial. Linear regression was performed for each session day individually. A 95% confidence interval of the mean was computed by performing the Student’s t -inverse cumulative distribution function over the ten sessions.

Kruskal–Wallis tuning analysis

As an alternative tuning definition, differences in firing rates between words were tested using the Kruskal–Wallis test, the non-parametric analogue to the one-way analysis of variance (ANOVA). For each neuron, the analysis was performed to evaluate the null hypothesis that data from each word come from the same distribution. A follow-up analysis was performed to adjust for FDR between the P values for each task phase 61 , 62 . A unit was defined as tuned during a phase if the adjusted P value was smaller than α  = 0.05.

Classification

Using the neuronal firing rates recorded during the tasks, a classifier was used to evaluate how well the set of words could be differentiated during each phase. Classifiers were trained using averaged firing rates over each task phase, resulting in six matrices of size n ,  m , where n corresponds to the number of trials and m corresponds to the number of recorded units. A model for each phase was built using LDA, assuming an identical covariance matrix for each word, which resulted in best classification accuracies. Leave-one-out CV was performed to estimate decoding performance, leaving out a different trial across neurons at each loop. PCA was applied on the training data, and PCs explaining more than 95% of the variance were selected as features and applied to the single testing trial. A 95% confidence interval of the mean was computed as described above.

Cross-phase classification

To estimate shared neural representations between different task phases, we performed cross-phase classification. The process consisted in training a classification model (as described above) on one of the task phases (for example, ITI) and to test it on the ITI, cue, imagined speech and vocalized speech phases. The method was repeated for each of the ten sessions individually, and a 95% confidence interval of the mean was computed. Significant differences in classification accuracies between phases decoded with the same model were evaluated using a paired two-tailed t -test. FDR correction of the P values was performed (‘Linear regression tuning analysis’) 61 , 62 .

Classification performance significance testing

To assess the significance of classification performance, a null dataset was created by repeating classification 100 times with shuffled labels. Then, different percentile levels of this null distribution were computed and compared to the mean of the actual data. Mean classification performances higher than the 97.5th percentile were denoted with P < 0.05 and higher than 99.5th percentile were denoted with P < 0.01.

dPCA analysis

dPCA was performed on the session data to study the activity of the neuronal population in relation to the external task parameters: cue modality and word. Kobak et al. 63 introduced dPCA as a refinement of their earlier dimensionality reduction technique (of the same name) that attempts to combine the explanatory strengths of LDA and PCA. By deconstructing neuronal population activity into individual components, each component relates to a single task parameter 64 .

This text follows the methodology outlined by Kobak et al. 63 . Briefly, this involved the following steps for N neurons:

First, unlike in PCA, we focused not on the matrix, X , of the original data, but on the matrices of marginalizations, X ϕ . The marginalizations were computed as neural activity averaged over trials, k , and some task parameters in analogy to the covariance decomposition done in multivariate analysis of variance. Since our dataset has three parameters: timing, t , cue modality, \(c\) (for example, auditory or visual), and word, w (eight different words), we obtained the total activity as the sum of the average activity with the marginalizations and a final noise term

The above notation of Kobak et al. is the same as used in factorial ANOVA, that is, \({X}_{{tcwk}}\) is the matrix of firing rates for all neurons, \(< \bullet { > }_{{ab}}\) is the average over a set of parameters \(a,b,\ldots\) , \(\bar{X}= < {X}_{{tcwk}}{ > }_{{tcwk}}\) , \({\bar{X}}_{t}= < {X}_{{tcwk}}-\bar{X}{ > }_{{cwk}}\) , \({\bar{X}}_{{tc}}= < {X}_{{tcwk}}-\bar{X}-{\bar{X}}_{t}-{\bar{X}}_{c}-{\bar{X}}_{w}{ > }_{{wk}}\) and so on. Finally, \({{{\epsilon }}}_{{tcwk}}={X}_{{tcwk}}- < {X}_{{tcwk}}{ > }_{k}\) .

Participant 1 datasets were composed of N  = 333 (SMG), N  = 828 (S1) and k  = 8. Participant 2 datasets were composed of N  = 547 (SMG), N  = 522 (S1) and k  = 16. To create balanced datasets, error trials were replaced by the average firing rate of k  − 1 trials.

Our second step reduced the number of terms by grouping them as seen by the braces in the equation above, since there is no benefit in demixing a time-independent pure task, \(a\) , term \({\bar{X}}_{a}\) from the time–task interaction terms \({\bar{X}}_{{ta}}\) since all components are expected to change with time. The above grouping reduced the parametrization down to just five marginalization terms and the noise term (reading in order): the mean firing rate, the task-independent term, the cue modality term, the word term, the cue modality–word interaction term and the trial-to-trial noise.

Finally, we gained extra flexibility by having two separate linear mappings \({F}_{\varphi }\) for encoding and \({D}_{\varphi }\) for decoding (unlike in PCA, they are not assumed to be transposes of each other). These matrices were chosen to minimize the loss function (with a quadratic penalty added to avoid overfitting):

Here, \({{\mu }}=(\lambda\Vert X\Vert)^{2}\) , where λ was optimally selected through tenfold CV in each dataset.

We refer the reader to Kobak et al. for a description of the full analytic solution.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are openly available via Zenodo at https://doi.org/10.5281/zenodo.10697024 (ref. 65 ). Source data are provided with this paper.

Code availability

The custom code developed for this study is openly available via Zenodo at https://doi.org/10.5281/zenodo.10697024 (ref. 65 ).

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Acknowledgements

We thank L. Bashford and I. Rosenthal for helpful discussions and data collection. We thank our study participants for their dedication to the study that made this work possible. This research was supported by the NIH National Institute of Neurological Disorders and Stroke Grant U01: U01NS098975 and U01: U01NS123127 (S.K.W., D.A.B., K.P., C.L. and R.A.A.) and by the T&C Chen Brain-Machine Interface Center (S.K.W., D.A.B. and R.A.A.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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S.K.W., D.A.B. and R.A.A. designed the study. S.K.W. and D.A.B. developed the experimental tasks and collected the data. S.K.W. analysed the results and generated the figures. S.K.W., D.A.B. and R.A.A. interpreted the results and wrote the paper. K.P. coordinated regulatory requirements of clinical trials. C.L. and B.L. performed the surgery to implant the recording arrays.

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The video shows the participant performing the internal speech task in real time. The participant is cued with a word on the screen. After a delay, an orange dot appears, during which the participant performs internal speech. Then, the decoded word appears on the screen, in green if it is correctly decoded and in red if it is wrongly decoded.

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Wandelt, S.K., Bjånes, D.A., Pejsa, K. et al. Representation of internal speech by single neurons in human supramarginal gyrus. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01867-y

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