Let’s look at all these categories of parts of speech with examples:
A name or title given to an object, person, group, or concept is known as a noun . It can either be the subject of a sentence (an individual who acts) or the object of the verb (receiver of the action).
Nouns can be further divided into common nouns (A generic term used to refer to somebody or something) and proper nouns (A specific name used to refer to an individual, place, or concept). The first letter of proper nouns has to always be capitalized, whereas the first letter of common nouns is only capitalized at the beginning of a sentence.
Other types of nouns include collective nouns, abstract nouns, and gerunds. Let’s look at the use of these nouns in a sentence.
To avoid repetition, pronouns are used as stand-ins for nouns. A pronoun is usually used to indicate a noun that is already mentioned. It can refer to people, places, objects, or concepts. Pronouns can further be divided into the following categories:
Let’s look at a few example sentences with these different types of pronouns:
A descriptive word that modifies a noun or pronoun is known as an adjective . It elaborates on characteristics and provides descriptions of the subject they modify. This may include physical characteristics, qualities, or quantity of the subject.
Adjectives can either be placed before or after nouns or pronouns. Here is an example:
My father gifted me a blue pen before my exams. It was a lovely pen with dark blue ink.
A word that indicates an action, an event, or a state of being is called a verb . It indicates the action the subject is performing by itself or on an object. A complete sentence must at least contain a subject and a verb.
Verbs can be altered according to the subject, tense, and tone of voice. They can further be divided into two categories:
Let’s take a look at examples of both these verbs:
Tara walked towards me and embraced me.
Jamil came to meet me.
An adverb is a descriptive word that gives more information about a verb, adjective, or another adverb. A rule of thumb to turn an adjective into an adverb is to simply add ly at the end. However, this rule is not applicable everywhere.
Adverbs can be further divided into the following types:
Here are a few examples of these adverbs in a sentence:
A conjunction is a word used to join two or more sentences, phrases, clauses, or words. There are three types of conjunctions :
Here are a few example sentences with all three types of conjunctions:
Most animals have a fight- or -flight response to potentially dangerous situations. (Coordinating conjunction)
Although it was snowing very heavily, the schools were still open. (Subordinating conjunction)
Both Trixie and Katya like to indulge in psychological thrillers. (Correlative conjunctions)
A preposition is a word or phrase that indicates the relationship of the noun or pronoun with the rest of the sentence. Prepositions can be used to indicate aspects of time, space, location, and direction. Here are a few example sentences with prepositions:
Sam is the head of the department.
Capybaras swim with their heads above the water.
Shall we meet by the river at 6 pm?
Interjections are exclamations that form a separate part of the sentence. They are used to indicate emotions such as awe, joy, pain, or hesitation. They can also be used as a command or a greeting. Here are some example sentences with interjections:
Wow! What a game.
Ouch! That hurt.
Psst! Do you have an extra pencil?
Hey! How are you today?
Shush! The baby is sleeping.
The following categories at one point were considered separate parts of speech, but are now more or less integrated with the other eight parts of speech. Let’s take a look.
Determiners are words that describe the qualities of a noun such as quantity, belonging as well as position. As per the traditional eight parts of speech, these are classified as adjectives or even pronouns.
Here are a few example sentences:
That is my chair.
Few people believe in the power of positive reinforcement.
We met plenty of tourists tourists in Bangkok, many of whom were from our city.
Articles are used to modify a noun to indicate if it is general or specific. There are two types of articles.
Here are some examples of these articles:
A cow was lazily grazing in the meadow.
He noticed that an eye of the pigeon was red.
Although articles can be classified as a separate part of speech, they are generally included under the category of determiners.
Certain words can function as multiple parts of speech depending on the way they’re used. Let’s look at a few example sentences with these words:
The word run can function as a verb, noun as well as an adjective depending on how it’s used. Here are a few example sentences with the word run used in different contexts.
Richard runs by the lake every morning. (Verb)
We should start going for evening runs together. (Noun)
Edgar scored the top grade but Violet certainly gave him a run for his money.(Adjective)
The word lead can function as a noun as well as an adjective. Here’s how it’s used in both these cases:
She is the only lead we have. (Noun)
The lead surgeon failed to show up for the operation. (Adjective)
Work can be used as a verb as well as a noun depending on the circumstances. Here are a few example sentences of work in both contexts:
I usually leave from work at 5:00 pm. (Noun)
You must work tirelessly to achieve success. (Verb)
These differences may seem trivial at first but are key to perfect writing. As editing and proofreading experts, we realize the importance of understanding grammar concepts for flawless writing.
We’ve created a useful list of resources to help you minimize such errors. We hope they help bring out the best in your words!
What are the eight parts of speech, how many parts of speech are there, eight or 9, how to identify parts of speech.
Found this article helpful?
Leave a Comment: Cancel reply
Your email address will not be published.
Your organization needs a technical editor: here’s why, your guide to the best ebook readers in 2024, writing for the web: 7 expert tips for web content writing.
Subscribe to our Newsletter
Get carefully curated resources about writing, editing, and publishing in the comfort of your inbox.
How to Copyright Your Book?
If you’ve thought about copyrighting your book, you’re on the right path.
© 2024 All rights reserved
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Scientific Reports volume 14 , Article number: 18922 ( 2024 ) Cite this article
19 Accesses
Metrics details
When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies coherent versus nonsense languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.
Introduction.
Electroencephalography (EEG) is a non-invasive method that can be used to study brain responses to sounds. Traditionally, unnatural periodic stimuli (e.g., click trains, modulated tones, repeated phonemes) are presented to listeners, and the recorded EEG signal is averaged to obtain the resulting brain response and to enhance its stimulus-related component 3 , 31 , 33 . These stimuli do not reflect everyday human natural speech, as they are repetitive, not continuous, and are thus processed differently by the brain 24 . Although these measures provide valuable insights about the auditory system, they do not provide insights about speech intelligibility. To investigate how the brain processes realistic speech, it is common to model the transfer function between the presented speech and the resulting brain response 11 , 18 . Such models capture the time-locking of the brain response to certain features of speech, often referred to as neural tracking. Three main model types are being used to measure the neural tracking of speech: (1) a linear regression model that reconstructs speech from EEG (backward modeling); (2) a linear regression model that predicts EEG from speech (forward modeling); and (3) classification tasks that associate synchronized segments of EEG and speech among multiple candidate segments 13 , 15 , 35 . For forward and backward models, the correlation between the ground truth and predicted/reconstructed signal provides the measure of neural tracking, while for the classification task, classification accuracy is utilized. Estimations of neural tracking with such models can be used to measure speech intelligibility. 40 showed a strong correlation between the neural tracking estimation obtained with linear models and speech intelligibility behavioural measurements.
To investigate how the brain processes speech, research has focused on different features of speech signals, which are known to be processed at different stages along the auditory pathway. Three main classes have hence been investigated:
Acoustics (e.g., spectrogram, speech envelope 18 , f0 34 , 39 )
Lexical segmentation features (e.g., phone onsets, word onsets, 17 , 30 )
Linguistics (e.g., phoneme surprisal, word frequency, 7 , 8 , 20 , 28 , 36 , 42 )
As opposed to neural tracking studies using broad features that carry mostly acoustic information, we here select linguistic features to narrow down our focus to speech understanding. Linguistic features of speech reflect information carried by a word or a phoneme, and their resulting brain response can be interpreted as a marker of speech understanding 7 , 20 . Considering the correlation between feature classes 12 , many studies accounted for the acoustic and lexical segmentation components of linguistic features 7 , 20 , while others did not 8 , 42 , potentially measuring the neural tracking of non-linguistic information.
Although the dynamics of the brain responses are known to be non-linear, most of the studies investigating neural tracking relied on linear models, which is a crude simplification. Later research attempted to introduce non-linearity, using deep neural networks. Such architectures relied on simple fully connected layers 14 , recurrent layers 2 , 32 , or even recently transformer-based architectures 15 . For a global overview of EEG-based deep learning studies see 35 .
Most deep learning work used low-frequency acoustic features, such as the Mel spectrogram, or the speech envelope 2 , 4 , or higher frequency features such as the fundamental frequency of the voice, f0 34 , 38 to improve the decoder’s performance. Although studies using invasive recording techniques showed the encoding of multiple linguistic features 26 , very few EEG-based deep learning studies involved linguistic features 15 . In a previous study 36 , we used a deep learning framework and measured additional neural tracking of linguistic features over lexical segmentation features in young healthy native Dutch speakers who listened to Dutch stimuli. This finding emphasized that a component of neural tracking corresponds to the phoneme or word rate, while another corresponds to the semantic context reflected in linguistic features. In addition, linear modeling studies 21 , 41 suggested the relationship between understanding and the added value of linguistics. 21 used two incomprehensible language conditions (i.e. Frisian, a West Germanic language of Friesland, and random-word-shuffling of Dutch speech) to manipulate speech understanding. However, within our deep learning framework, no investigations have been conducted on language data incomprehensible to the test subject.
In this article, we aim to investigate the impact of language understanding on the neural tracking of linguistics using our above-mentioned deep learning framework. Therefore, we fine-tune and evaluate our previously published deep learning framework to measure the added value of linguistics over lexical segmentation features on the neural tracking of three different stimuli: (1) Dutch, (2) Frisian, and (3) scrambled Dutch words. Additionally, we evaluate our model on a language classification task to explore whether our CNN can learn language-specific brain responses.
In 21 , 19 participants were recruited (6 men and 13 women; mean age ± std = 22 ± 3 years). We included participants that had normal hearing and Dutch as their native language. Participants with attention problems, learning disabilities or sever head trauma were excluded. The latter were identified via a questionnaire. Pure tone audiometry was conducted at octave frequencies from 125 to 8000 Hz to assess the hearing capacity. Participants for whom a hearing threshold exceeded 20 dB HL, were excluded from this study.
The participants listened to a comprehensible story in Dutch, a list of scrambled words in Dutch, and an incomprehensible story in Frisian. The three stories were narrated by the same male native Dutch speaker, who learnt Frisian as a second language. The Dutch story is derived from a podcast series about crime cases, and the Frisian story is a translation of the Dutch. Frisian is a language related to Dutch but poorly understood by Dutch native participants who have no prior knowledge of it. The list of scrambled words consists of randomly shuffled words from the Dutch story. This story plays the role of intermediary comprehension as words are in Dutch (understood), however there is no sentence structure.
The duration of the Dutch, scrambled Dutch and Frisian stories, from now on referred to as “nguage conditions”are 10, 9, and 7 min, respectively. For the Dutch story, the participants listened to it entirely without any break, and had to answer a content question to make sure they paid attention. The Frisian and the scrambled Dutch story were presented in fragments of 2 min, with a word identification task at the end of each fragment to ensure focus. For more details, see 21 .
For the pre-training of our model, we use an additional dataset from 36 , containing EEG of 60 young healthy native Dutch participants listening to 8 to 10 audiobooks of 14 min each.
This study relates 4 features from EEG signals only including the linguistic features that showed a benefit over lexical segmentation features 36 .
The investigated lexical segmentation features are: the onset of any phoneme (PO) and of any word (WO). We then tested the added value of the following linguistic features:
Cohort entropy (CE), over PO
Word frequency (WF), over WO
on our model’s performance, measuring the neural tracking of speech.
Example phoneme-level features are depicted in Figure 1 a, and word-level features in Figure 1 b.
Lexical segmentation features: Time-aligned sequences of phonemes and words were extracted by performing a forced alignment of the identified phonemes 19 . PO and WO are the resulting one-dimensional arrays with pulses on the onsets of, respectively, phonemes and words. Silence onsets were set to 0 for both phonemes and words.
Active cohort of words: Before introducing cohort entropy, the active cohort of words must be defined. Following previous studies’ definition 7 , 20 , it is a set of words that starts with the same acoustic input at any point in the word. For example, should we find cohorts in English, the active cohort of words for the phoneme / n / in “ban” corresponds to the ensemble of words that exist in that language starting with “ban” (e.g., “banned”,“bandwidth” etc.). For each phoneme, the active cohort was determined by taking word segments that started with the same phoneme sequence from the lexicon.
Lexicon: For the Dutch language, the lexicon for determining the active cohort was based on a custom word-to-phoneme dictionary (9082 words). As some linguistic features are based on the word frequency in Dutch, the prior probability for each word was computed, based on its frequency in the SUBTLEX-NL database 27 .
For the Frisian language, the word-to-phoneme dictionary (75036 words) and the word frequencies were taken from 43 .
Cohort entropy: CE reflects the degree of competition among possible words that can be created from the active cohort including the current phoneme. It is defined as the Shannon entropy of the active cohort of words at each phoneme as explained in 7 (see Equation 1 ). \(CE_{i}\) is the entropy at phoneme i and \(p_{word}\) is the probability of the given word in the language. The sum iterates over words from the active cohort \(cohort_{i}\) .
Word frequency: For the Dutch language, the prior probability for each word was based on its frequency in the SUBTLEX-NL database 27 . Values corresponding to words not found in the SUBTLEX-NL were set to 0.
For the Frisian language, the word probabilities were used from 43 . WF is a measure of how frequently a word occurs in the language and is defined in Equation 2 .
More details about their implementation can be found in previous studies 6 , 20 , 36 .
Visualization of word- and phoneme-level lexical segmentation and linguistic features. ( a ) Cohort entropy is depicted in yellow, phoneme onset in black, over a 5 s window. ( b ) Word frequency is depicted in yellow, word onset in black, over a 10 s window.
The EEG was initially downsampled using an anti-aliasing filter from 8192 to 128 Hz to decrease the processing time. A multi-channel Wiener filter 37 was then used to remove eyeblink artifacts, and re-referencing was performed to the average of all electrodes. The resulting signal was band-pass filtered between 0.5 and 25 Hz using a Least Squares filter of order 5000 for the high-pass filter, and 500 for the low-pass filter, with 10% transition bands (transition of frequencies 10% above the lowpass filter and 10% below the highpass) and compensation for the group delay. We then downsampled the signal to 64 Hz.
Lexical segmentation and linguistic features are discrete representation, namely vectors of zero and nonzero values. They were calculated at 64 Hz and no further pre-processing was needed.
In this study, we use the performance of the match-mismatch (MM) classification task 13 to measure the neural tracking of different speech features (Figure 2 ). We use the same paradigm as 36 . The model is trained to associate the EEG segment with the matched speech segment among two presented speech segments. The matched speech segment is synchronized with the EEG, while the mismatched speech segment occurs 1 second after the end of the matched segment. These segments are of fixed length, namely 10 s for word-based features and 5 s for phoneme-based features, to provide enough context to the models as hypothesized by 36 . This task is supervised since the matched and mismatched segments are labeled. The evaluation metric is classification accuracy.
Match-mismatch classification task. The match-mismatch task is a binary classification paradigm that associates the herewith blue EEG and speech segments. The matched speech segment is synchronized with the EEG (blue segment), while the mismatched speech occurs 1 second after the end of the matched segment (black segment). The figure depicts segments of 5 s and 10 s, which will be lengths used in our studies for the phoneme and word levels respectively.
In 36 , we developed a multi-input convolutional neural network (MICNN) model that aims to relate different features of the presented speech to the resulting EEG. The MICNN model has 127k parameters and is trained using binary cross-entropy as its loss function (Adam optimizer, 50 epochs, learning rate: \(10^-{3}\) ). We used early stopping as regularization. It is trained to perform well on the MM task presented in "The match-mismatch task ". Through the MM task, the MICNN model learns to measure the neural tracking of speech features, which we can thereafter use to quantify the added value of one speech feature over another. By inputting multiple features, we make sure to account for redundancies and correlations between them and enable interpretation of what information makes the model better on the MM task. In our case, our models enable us to quantify the added value of a given linguistic feature (WF or CE) over their corresponding lexical segmentation feature (WO or PO, respectively)
To ensure that the model has enough data to identify a typical neural response to Dutch linguistic features, we always train the MICNN model on the dataset used in 36 as a first step. We use an identical training procedure.
We performed two fine-tuning conditions: one subject-independent (language fine-tuning) and one subject-dependent (subject fine-tuning). For both, we keep the training parameters mentioned in " Multi-input features convolutional neural network ", and solely change the data used for training and evaluation.
For the language condition fine-tuning, we trained a separate model for each subject, including data from the other 25 subjects, for each of the three language conditions (i.e., Dutch, Frisian, and scrambled Dutch): We exclude a selected subject and separate the data from the 25 other subjects in an 60%/20%/20% training/validation/test split. For the 25 other subjects, the first and last 30% of their recording segment were used for training. The first half of the remaining 40% was used for validation (i.e., for regularization) and the second half to get an estimate of the accuracy of unseen speech data. Once the model is fine-tuned, we evaluate it on the selected subject.
For the subject-specific fine-tuning, a 25%/25%/50% training/validation/test split was performed. Compared to the language condition fine-tuning, selecting the data of a single subject divides the amount of data by a factor of 26 (i.e., the number of subjects) for training validation and testing. For the Dutch, Frisian and scrambled Dutch stories, the total amount of data is thus 10 min, 7 min and 9 min respectively. We thus modified the split ratio to increase the amount of data in the validation set to enable keeping the batch size constant across fine-tuning conditions. We used the validation set for regularization. The first and last 12.5% of the recording segment were used for training. The first third of the remaining 75% was used for validation and the two remaining thirds for testing. For each set (training, validation, and testing), each EEG channel was then normalized by subtracting the mean and dividing by the standard deviation.
Inspired by the support vector machine (SVM) utilization for aphasia classification from 10 , we use the MM accuracy obtained with four models to classify the language presented to the participant. The four models are the following: the control (word onset or phoneme onset) and linguistic (cohort entropy or word frequency) models for both fine-tuning conditions. We chose to use only the fine-tuned conditions as the non-fine-tuned one was biased towards giving better performance on the Dutch condition. These four MM accuracy values constitute the features provided to the SVM to solve a one-vs-one classification: did the person listen to one or another selected language condition. We consider three language conditions: Dutch, scrambled Dutch, and Frisian, which in total leads to three binary classification tasks.
We used a radial basis function kernel SVM and performed a nested cross-validation approach. In the inner cross-validation, the C-hyperparameter (determining the margin) and pruning were optimized (accuracy-based) and tested in a validation set using 5-fold cross-validation. Predictions were made on the test set in the outer loop using leave-one-subject-out cross-validation. We computed the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC), and further reported the accuracy, F1-score, sensitivity, and specificity of the classifier.
We only depict results with language or subject fine-tuning as pure evaluation would potentially give a performance advantage to the model on Dutch because of the pre-training on Dutch stimuli. We still show the non-fine-tuned results in Appendix A.
Although not significant, at both the word and the phoneme levels, the neural tracking when adding the linguistics on top of lexical segmentation features is typically higher at the group level. We depict in Appendix B (see Figure B1a and B1b) the models performances at the phoneme and the word levels across stimuli.
Figure 3 depicts for all three stimuli, the difference in the MM accuracy between L and C conditions for phoneme-level features. We observed no significant difference when comparing the Frisian and the Dutch conditions (Wilcoxon signed-rank test, \(W=172, \textit{p}=0.94\) ), the Dutch and the Sc. Dutch conditions (Wilcoxon signed-rank test, \(W=160, \textit{p}=0.73\) ), and the Frisian and the Sc. Dutch conditions (Wilcoxon signed-rank test, \(W=149, \textit{p}=0.51\) ).
We also depict for all three stimuli, the L-C accuracy at the word level. We observed a significant increase of the L-C accuracy of Sc. Dutch over Frisian (Wilcoxon signed-rank test, \(W=97, \textit{p}=0.046\) ), however no significant difference in the Dutch-Frisian and Dutch-Sc. Dutch comparisons (Wilcoxon signed-rank test, Dutch-Frisian: \(W=104, \textit{p}=0.07\) , Dutch-Sc. Dutch: \(W=163, \textit{p}=0.78\) ).
To see whether the model could be improved by introducing subject information, we add a subject fine-tuning step in the next Section (for details about the method, see " Fine-tuning and evaluation on validation datasets ").
L - C accuracy for three stimuli with a language-finetuned model. L corresponds to the MM accuracy obtained by (1) the cohort entropy model at the phoneme level; (2) the word frequency model at the word level. C corresponds to the MM accuracy obtained by (1) the phoneme onset model at the phoneme level; (2) the word onset model at the word level. Significance levels : \(p<0.05\) : *.
We depict results up to half of the recording length of the shortest stimulus for each subject (i.e., 3.5 min) as we used the other half to fine-tune the model.
Figure 4 depicts for all three stimuli, the L-C accuracy at the phoneme level. We observed no significant difference in the L-C accuracy in the Frisian-Sc. Dutch, Dutch-Sc.-Dutch and Frisian-Dutch comparisons (Wilcoxon signed-rank test, \(W=141, \textit{p}=0.39\) , and \(W=118, \textit{p}=0.15\) , and \(W=152, \textit{p}=0.78\) respectively).
We also depict for all three stimuli, the L-C accuracy at the word level. We observed a significant increase in the L-C accuracy of Dutch over Frisian (Wilcoxon signed-rank test, \(W=97, \textit{p}=0.046\) ). We observed no significant difference in the L-C accuracy for the other comparisons (Wilcoxon signed-rank test, Dutch-Frisian: Dutch-Sc.Dutch: \(W=134, \textit{p}=0.30\) , and Sc.Dutch-Frisian: \(W=144, \textit{p}=0.44\) ).
L - C accuracy across recording lengths for three stimuli with a subject-finetuned model. L corresponds to the MM accuracy obtained by (1) the cohort entropy model at the phoneme level; (2) the word frequency model at the word level. C corresponds to the MM accuracy obtained by (1) the phoneme onset model at the phoneme level; (2) the word onset model at the word level. Significance levels : \(p<0.05\) : *.
Figure 5 depicts the SVM classification results of the three binary classification tasks: (1) Dutch vs. Frisian, (2) Dutch vs. Scrambled Dutch, and (3) Frisian vs. Scrambled Dutch. For more details about the methods, see " Language condition classification" .
When evaluated over all subjects, our SVM classifier correctly classified the scrambled Dutch from the Frisian condition with an accuracy of 61.5% for both the FTL and FTS conditions. In addition, the classifier correctly classified the scrambled Dutch from the Dutch condition with an accuracy of 69.23% and 71.15% for the FTL and FTS conditions, respectively. We do not show the results for the Dutch vs. Frisian task, as the classifier performed close to the chance level.
SVM performance across fine-tuning conditions. The performance is depicted for each condition as a ROC curve plotting the true positive rate as a function of the false positive rate. ( a ) Scrambled Dutch vs. Frisian classification with language fine-tuning; ( b ) with subject fine-tuning; ( c ) Scrambled Dutch vs. Dutch classification with language fine-tuning; ( d ) with subject fine-tuning.
We evaluated a deep learning framework that measures the neural tracking of linguistics on top of lexical segmentation features in different language understanding conditions. Although we used the same dataset, direct comparison with 21 is difficult, considering the difference in the models, and the features provided to the model.
As our model was trained uniquely on Dutch before, the model might not have learned the typical brain response to Frisian linguistics, or scrambled Dutch, thus leading to overfitting on Dutch, impairing the objective measure of linguistics tracking on other language conditions. To avoid this bias, we fine-tuned our model on Frisian and scrambled Dutch data before respective evaluations.
Since we are interested in the linguistics added value over lexical segmentation features, we compared the difference between the linguistic and lexical segmentation models’ performance across language conditions. For cohort entropy, although there is no significant difference between language conditions in the linguistics added value, the one of Frisian is systematically lower. For word frequency, we observed a significant increase in the added value of linguistics for scrambled Dutch over Frisian. In addition, although not significantly different, the linguistics added value also appeared lower for Frisian than Dutch. This finding suggests that a language that is not understood might show a lower linguistics added value. Regarding the scrambled Dutch results performing non-significantly different than Dutch, although the subjective rating of understanding was very low, individual words are still in Dutch and thus understood. Cohort entropy and word frequency are features that are independent of the order of words in the sentence, which might explain why we do not observe a drop in the neural tracking of linguistics.
Language processing in the brain is influenced by our memory, and top-down processing 23 , 29 , and might thus have a strong subject-specific component in the response to linguistic features. We therefore decided to fine-tune the models on each subject before evaluation on top of the language fine-tuning. The only significant difference we observed was for word frequency between Dutch and Frisian. This finding supports the conclusion drawn with language fine-tuning: the added value of linguistics is larger when the language is understood. We note that the subject fine-tuning diminished the data used per subject by 50% (i.e., up to 3.5 min of recording) for evaluation, which might not be sufficient to get a good estimate of the accuracy. We therefore do not interpret further the subject fine-tuning condition.
With SVM classifiers, we were able, from the match-mismatch accuracies on our different features, to classify the Frisian vs. the scrambled Dutch condition, as well as the Dutch vs. the scrambled Dutch condition. This suggests that neural tracking of linguistics and lexical segmentation features differs between continuous and scrambled speech. We expected the classifier to be able to differentiate Frisian from Dutch, as participants were not Frisian speakers. Our hypotheses to explain this phenomenon are fourfold: (1) Frisian is a language that is too similar to Dutch to measure a difference in linguistic tracking, therefore there is some understanding by the participants, as emphasized by the subjective rating from 21 (the authors reported that the speech understanding median subjective rating for the Dutch condition was 100%, while the Frisian and scrambled Dutch were 50% and 10.5%, respectively. The value for Frisian is strangely high and we believe it might in reality be lower.). On the other hand, we believe that within our framework, choosing a similar language is advisable. A very different language (e.g., Mandarin), could have caused a decrease of neural tracking for both lexical segmentation and linguistic features, obliterating our method relying on the added value of linguistics; (2) Linguistic and lexical segmentation features are too correlated, notably because their only differ in the magnitude, which might be too limited to describe the language complexity (3) the magnitude of linguistics has a distribution that tends to be skewed towards the value 1 (i.e., the magnitude of lexical segmentation features) in our three stimuli (see 21 ). A more controlled speech content (e.g., sentences with uncommon words) might make the impact of linguistics larger; (4) An additional concern can be added for word frequency: the most frequent words in the language are non-content words (e.g., “and”, “or”, “of”). In addition, most of these words are short words. The model might therefore have learnt a spurious content vs. non-content words threshold from the word frequency, which can be globally narrowed-down to short vs. long words. The length of words can possibly be derived from the word onsets as well. The model could therefore simply use word onset information and omit the magnitude provided by linguistics, which would explain the low benefit of adding word frequency over word onsets.
A possible shortcoming of our training paradigm is the use of a single language for pre-training (i.e., Dutch), which might provide the fine-tuning insufficient abilities to generalize to other language conditions. To solve this issue and preserve a necessary pre-training step for complex deep learning frameworks, we could change our experimental paradigm by: (1) keeping the same language across understanding conditions to avoid biasing the model during pre-training; and (2) avoid random word-shuffling to preserve the word context in sentences. Other non-understanding conditions could involve vocoded speech or degraded-SNR speech as done in 1 . We also evaluated our framework to a speech rate paradigm 41 . However, although we observed a decreased neural tracking of linguistics in challenging listening scenarios (i.e., very high speech rates), we also observed an equivalent decreased neural tracking of lexical segmentation features. We could thus not draw any conclusions whether the nature of this decrease was acoustic or linguistic.
Another pitfall in our comparison across languages is that our linguistic features both rely on word frequencies. The word frequency values were therefore calculated for Dutch and Frisian, respectively. Our participants being Dutch speakers not speaking Frisian, have an language representation in the brain corresponding to the Dutch word frequencies, and not to the Frisian one. This might thus result in a lower neural tracking of linguistics when listening to Frisian content compared to Dutch content.
Linguistic features, as we use them now, are very constrained: they mainly give information about the word or phoneme frequency in the language. Language models are known to capture more information. As an example, the Bidirectional Transformers for Language Understanding (i.e., BERT) 16 model carries phrase-level information in its early layers, surface (e.g., sentence length) and syntactic (e.g., word order) information in the intermediate layers and semantic features (e.g., subject-verb agreement) in the late layers 25 . Such representations could contain more detailed information about the language than our current linguistic features. 15 used larger pre-trained speech encoder models, and following up on this work, we could use language model layers, providing information about the structure of language that can be related to brain responses 9 , 22 .
In this article, we investigated the impact of language understanding on the neural tracking of linguistics. We demonstrated that our previously developed deep learning framework can classify coherent from nonsense languages using the neural tracking of linguistics. We explored the ability and the limitations of state-of-the-art linguistic features to objectively measure speech understanding using lexical segmentation features as our acoustic tracking baseline. Our findings along with the current literature support the idea that, considering this framework, further work should be dedicated to (1) designing new linguistic features using recent powerful language models, and (2) using incomprehensible and comprehensible speech stimuli from the same language, to facilitate the comparison across conditions.
The data that support the findings of this study can be made available from the corresponding author on reasonable request, so far as this is in agreement with privacy and ethical regulations. A subset of the pretraining dataset (i.e., 60 subjects, 10 stories) was published and is available online 5 .
Accou, B., Monesi, M. J., Hamme, H. V. & Francart, T. Predicting speech intelligibility from EEG in a non-linear classification paradigm. J. Neural Eng. 18 , 066008. https://doi.org/10.1088/1741-2552/ac33e9 (2021).
Article ADS Google Scholar
Accou, B., Van Vanthornhout, J., Hamme, H. & Francart, T. Decoding of the speech envelope from EEG using the VLAAI deep neural network. Sci. Rep. 13 (1), 812. https://doi.org/10.1038/s41598-022-27332-2 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Anderson, S., Parbery-Clark, A., White-Schwoch, T. & Kraus, N. Auditory brainstem response to complex sounds predicts self-reported speech-in-noise performance. J. Speech Lang. Hear. Res. 56 (1), 31–43. https://doi.org/10.1044/1092-4388(2012/12-0043) (2013).
Article PubMed Google Scholar
Bollens, L., Francart, T., Hamme Van, H. Learning subject-invariant representations from speech-evoked eeg using variational autoencoders. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 1256–1260, (2022). https://doi.org/10.1109/ICASSP43922.2022.9747297 .
Bollens, L., Accou, B., Van Hamme, H., Francart, T. A Large Auditory EEG decoding dataset, (2023). https://doi.org/10.48804/K3VSND
Brodbeck, C. & Simon, J. Z. Continuous speech processing. Curr. Opin. Physio. 18 , 25–31. https://doi.org/10.1016/j.cophys.2020.07.014 (2020).
Article Google Scholar
Brodbeck, C., Hong, L. E. & Simon, J. Z. Rapid transformation from auditory to linguistic representations of continuous speech. Curr. Biol. 28 (24), 3976-3983.e5. https://doi.org/10.1016/j.cub.2018.10.042 (2018).
Article CAS PubMed PubMed Central Google Scholar
Broderick, M. P., Anderson, A. J., Di Liberto, G. M., Crosse, M. J. & Lalor, E. C. Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech. Curr. Biol. 28 (5), 803-809.e3. https://doi.org/10.1016/j.cub.2018.01.080 (2018).
Article CAS PubMed Google Scholar
Caucheteux, C. & King, J.-R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5 (1), 134. https://doi.org/10.1038/s42003-022-03036-1 (2022).
Article PubMed PubMed Central Google Scholar
De Clercq, P., Puffay, C., Kries, J., Van Hamme, H., Vandermosten, M., Francart, T, Vanthornhout, J. Detecting post-stroke aphasia via brain responses to speech in a deep learning framework, arXiv:2401.10291 (2024).
Crosse, M. J., Di Liberto, G. M., Bednar, A. & Lalor, E. C. The multivariate temporal response function (mTRF) toolbox: A MATLAB toolbox for relating neural signals to continuous stimuli. Front. Hum. Neurosci. 10 (NOV2016), 1–14. https://doi.org/10.3389/fnhum.2016.00604 (2016).
Daube, C., Ince, R. A. A. & Gross, J. Simple acoustic features can explain phoneme-based predictions of cortical responses to speech. Curr. Biol. 29 (12), 1924–19379. https://doi.org/10.1016/j.cub.2019.04.067 (2019).
de Cheveigné, A., Slaney, M., Fuglsang, S. A. & Hjortkjaer, J. Auditory stimulus-response modeling with a match-mismatch task. J. Neural Eng. 18 (4), 046040. https://doi.org/10.1088/1741-2552/abf771 (2021).
de Taillez, T., Kollmeier, B. & Meyer, B. T. Machine learning for decoding listeners’ attention from electroencephalography evoked by continuous speech. Eur. J. Neurosci. 51 (5), 1234–1241. https://doi.org/10.1111/ejn.13790 (2020).
Défossez, A., Caucheteux, C., Rapin, J., Kabeli, O. & King, J. R. Decoding speech perception from non-invasive brain recordings. Nat. Mach. Intell. 5 (10), 1097–1107. https://doi.org/10.1038/s42256-023-00714-5 (2023).
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 4171–4186, Minneapolis, Minnesota, June (2019). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423 .
Di Liberto, G. M., O’Sullivan, J. A. & Lalor, E. C. Low-frequency cortical entrainment to speech reflects phoneme-level processing. Curr. Biol. 25 (19), 2457–2465. https://doi.org/10.1016/J.CUB.2015.08.030 (2015).
Ding, N, Simon, J.Z. Emergence of neural encoding of auditory objects while listening to competing speakers. Proceedings of the National Academy of Sciences 109 (29), 11854–11859 https://doi.org/10.1073/PNAS.1205381109 (2012).
Duchateau, J., Kong, Y., Cleuren, L., Latacz, L., Roelens, J., S., Abdurrahman, D., Kris, G., Pol, V., Werner & Van hamme, H. Developing a reading tutor : design and evaluation of dedicated speech recognition and synthesis modules, (2009). ISSN 1872-7182.
Gillis, M., Van Canneyt, J., Francart, T. & Vanthornhout, J. Neural tracking as a diagnostic tool to assess the auditory pathway. bioRxiv , (2022). https://doi.org/10.1101/2021.11.26.470129 .
Gillis, M., Vanthornhout, J. & Francart, T. Heard or understood? neural tracking of language features in a comprehensible story, an incomprehensible story and a word list. eNeuro https://doi.org/10.1523/ENEURO.0075-23.2023 (2023).
Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25 (3), 369–380. https://doi.org/10.1038/s41593-022-01026-4 (2022).
Gwilliams, L. & Davis, M. H. Extracting language content from speech sounds: The information theoretic approach 113–139 (Springer, Cham, 2022).
Google Scholar
Hullett, P. W., Hamilton, L. S., Mesgarani, N., Schreiner, C. E. & Chang, E. F. Human superior temporal gyrus organization of spectrotemporal modulation tuning derived from speech stimuli. J. Neurosci. 36 (6), 2014–2026 (2016).
Jawahar, G., Sagot, B., Seddah, D. What does BERT learn about the structure of language? In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics , Florence, Italy, July (2019). https://inria.hal.science/hal-02131630 .
Keshishian, M. et al. Joint, distributed and hierarchically organized encoding of linguistic features in the human auditory cortex. Nat. Hum. Behav. 7 (5), 740–753. https://doi.org/10.1038/s41562-023-01520-0 (2023).
Keuleers, E., Brysbaert, M. & New, B. S. A new measure for Dutch word frequency based on film subtitles. Behav. Res. Methods 42 (3), 643–650 (2010).
Koskinen, M., Kurimo, M., Gross, J., Hyvärinen, A. & Hari, R. Brain activity reflects the predictability of word sequences in listened continuous speech. Neuroimage 219 , 116936. https://doi.org/10.1016/j.neuroimage.2020.116936 (2020).
Gwilliams, D. P. L., Marantz, A. & King, J.-R. Top-down information shapes lexical processing when listening to continuous speech. Lang. Cognit. Neurosci. https://doi.org/10.1080/23273798.2023.2171072 (2023).
Lesenfants, D., Vanthornhout, J., Verschueren, E & Francart, T. Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech. bioRxiv , (2019). https://doi.org/10.1101/551218 .
McGee, T. J. & Clemis, J. D. The approximation of audiometric thresholds by auditory brain stem responses. Otolaryngol. Head Neck Surg. 88 (3), 295–303. https://doi.org/10.1177/019459988008800319 (1980).
Monesi, M.J., Accou, B., Montoya-Martinez, J., Francart, T., Van Hamme, H. An LSTM Based Architecture to Relate Speech Stimulus to Eeg. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020-May (637424): 941–945, (2020). ISSN 15206149. https://doi.org/10.1109/ICASSP40776.2020.9054000 .
Picton, T. W., Dimitrijevic, A., Perez-Abalo, M.-C. & Van Roon, P. Estimating audiometric thresholds using auditory steady-state responses. J. Am. Acad. Audiol. 16 (03), 140–156. https://doi.org/10.3766/jaaa.16.3.3 (2005).
Puffay, C., Van Canneyt, J., Vanthornhout, J., Van hamme, H. & Francart, T. 2022 Relating the fundamental frequency of speech with EEG using a dilated convolutional network. In 23rd Annual Conf. of the Int. Speech Communication Association (ISCA)—Interspeech 4038–4042 (2022).
Puffay, C. et al. Relating EEG to continuous speech using deep neural networks: A review. J. Neural Eng. 20 (4) 041003. https://doi.org/10.1088/1741-2552/ace73f (2023).
Puffay, C. et al. Robust neural tracking of linguistic speech representations using a convolutional neural network. J. Neural Eng. 20 (4), 046040. https://doi.org/10.1088/1741-2552/acf1ce (2023).
Somers, B., Francart, T. & Bertrand, A. A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. J. Neural Eng. 15 (3), 036007. https://doi.org/10.1088/1741-2552/aaac92 (2018).
Article ADS PubMed Google Scholar
Thornton, M., Mandic, D., Reichenbach, T. Relating eeg recordings to speech using envelope tracking and the speech-ffr. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 1–2, (2023). https://doi.org/10.1109/ICASSP49357.2023.10096082 .
Van Canneyt, J., Wouters, J. & Francart, T. Neural tracking of the fundamental frequency of the voice: The effect of voice characteristics. Eur. J. Neurosci. 53 (11), 3640–3653. https://doi.org/10.1111/ejn.15229 (2021).
Vanthornhout, J., Decruy, L., Wouters, J., Simon, J. Z. & Francart, T. Speech intelligibility predicted from neural entrainment of the speech envelope. JARO - J. Assoc. Res. Otolaryngol. 19 (2), 181–191. https://doi.org/10.1007/s10162-018-0654-z (2018).
Verschueren, E., Gillis, M., Decruy, L., Vanthornhout, J. & Francart, T. Speech understanding oppositely affects acoustic and linguistic neural tracking in a speech rate manipulation paradigm. J. Neurosci. 42 (39), 7442–7453. https://doi.org/10.1523/JNEUROSCI.0259-22.2022 (2022).
Weissbart, H., Kandylaki, K. & Reichenbach, T. Cortical tracking of surprisal during continuous speech comprehension. J. Cognit. Neurosci. 32 , 1–12 (2019).
Yılmaz, E. et al. Open Source Speech and Language Resources for Frisian. Proc. Interspeech 2016 , pages 1536–1540, (2016). https://doi.org/10.21437/Interspeech.2016-48 .
Download references
The authors thank all the participants for the recordings, as well as Wendy Verheijen, Marte De Jonghe, Kyara Cloes, Amelie Algoet, Jolien Smeulders, Lore Kerkhofs, Sara Peeters, Merel Dillen, Ilham Gamgami, Amber Verhoeven, Lies Bollens, Vitor Vasconcelos and Amber Aerts for their help with data collection. Funding was provided by FWO fellowships to Bernd Accou (1S89622N), Marlies Gillis (1SA0620N; additional Internal Funds KU Leuven: PDMT1/23/011), Corentin Puffay (1S49823N), Pieter De Clercq (1S40122N), and Jonas Vanthornhout (1290821N).
Authors and affiliations.
Department Neurosciences, KU Leuven, ExpORL, Leuven, Belgium
Corentin Puffay, Jonas Vanthornhout, Marlies Gillis, Pieter De Clercq, Bernd Accou & Tom Francart
Department of Electrical engineering (ESAT), KU Leuven, PSI, Leuven, Belgium
Corentin Puffay, Bernd Accou & Hugo Van hamme
You can also search for this author in PubMed Google Scholar
C.P. wrote the manuscript, prepared figures and did the analyses, as well as the interpretation of the results present in the article. J.V. provided the main guidance, was heavily involved in the thinking process, and in the interpretation of the results. M.G. shared the data from her publication, provided help to C.P. in the preprocessing of data, was involved in the thinking process, and the interpretation of the results. P.DC. was involved in the thinking process and wrote the scripts for the SVM classification task. B.A. was involved in the thinking process, and provided the basis of the deep learning framework code. H.VH. and T.F. provided guidance and were involved in the interpretation of the results. All authors reviewed the manuscript.
Correspondence to Corentin Puffay or Tom Francart .
Competing interests.
The authors declare no competing interests.
Publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Below is the link to the electronic supplementary material.
Rights and permissions.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .
Reprints and permissions
Cite this article.
Puffay, C., Vanthornhout, J., Gillis, M. et al. Classifying coherent versus nonsense speech perception from EEG using linguistic speech features. Sci Rep 14 , 18922 (2024). https://doi.org/10.1038/s41598-024-69568-0
Download citation
Received : 15 April 2024
Accepted : 06 August 2024
Published : 14 August 2024
DOI : https://doi.org/10.1038/s41598-024-69568-0
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.
IMAGES
COMMENTS
The four types of speeches are manuscript, memorized, extemporaneous, and impromptu. Our aim is to acquaint you with these four different modes of delivery, to provide suggestions for when you are asked to make impromptu remarks, and then to focus most your time on the preparation, practice, and presentation of extemporaneous speeches.
Resources for demonstration speeches. 1. How to write a demonstration speech Guidelines and suggestions covering:. choosing the best topic: one aligning with your own interests, the audience's, the setting for the speech and the time available to you; how to plan, prepare and deliver your speech - step by step guidelines for sequencing and organizing your material plus a printable blank ...
This type of speech is seen when the President makes speeches off of a teleprompter and is an effective way to stay on target while speaking. Tips for manuscript speech include: Using the large ...
Informative speech. Informative speeches aim to educate an audience on a particular topic or message. Unlike demonstrative speeches, they don't use visual aids. They do, however, use facts, data and statistics to help audiences grasp a concept. These facts and statistics help back any claims or assertions you make.
Learning Objectives. Identify the four types of speech delivery methods and when to use them. There are four basic methods of speech delivery: manuscript, memorized, impromptu, and extemporaneous. We'll look at each method and discuss the advantages and disadvantages of each.
Ethos refers to an appeal to your audience by establishing your authenticity and trustworthiness as a speaker. If you employ pathos, you appeal to your audience's emotions. Using logos includes the support of hard facts, statistics, and logical argumentation. The most effective speeches usually present a combination these rhetorical strategies.
Step 4: Practice, practice, practice. The more you practice your speech the more you'll discover which sections need reworked, which transitions should be improved, and which sentences are hard to say. You'll also find out how you're doing on length. Step 5: Update, practice, and revise your speech until it has a great flow and you feel ...
1. Informative Speech. An informative speech is designed to educate the audience on a particular topic. The goal is to provide the audience with new information or insights and increase their understanding of the topic. The speech should be well-researched, organized, and delivered in a clear and engaging manner. 2.
The four main types of public speaking are informative, persuasive, demonstrative, and ceremonial. Understanding these will help specialize how you speak to the intention of your speech. The article goes over all the basic information, examples, and key things to note when delivering these types of speeches. Public speaking is multifaceted.
Just like essays, all speeches have three main sections: the introduction, the body, and the conclusion. However, unlike essays, speeches must be written to be heard as opposed to being read. You need to write a speech in a way that keeps the attention of an audience and helps paint a mental image at the same time. ... Whatever type of speech ...
Contestants wrote essays on a given theme, to create a speech at a specific time length (e.g.: three minutes). The essay was memorized and the delivery was judged by 1) the quality of the writing, 2) the accuracy with which it was recited; and 3) the precise length of time. Such contests seem archaic by today's more casual and somewhat less ...
Argumentative essays test your ability to research and present your own position on a topic. This is the most common type of essay at college level—most papers you write will involve some kind of argumentation. The essay is divided into an introduction, body, and conclusion: The introduction provides your topic and thesis statement
Key Takeaways. There are four main kinds of speech delivery: impromptu, extemporaneous, manuscript, and memorized. Impromptu speaking involves delivering a message on the spur of the moment, as when someone is asked to "say a few words.". Extemporaneous speaking consists of delivering a speech in a conversational fashion using notes.
Persuasive Speeches. A persuasive speech attempts to influence or reinforce the attitudes, beliefs, or behavior of an audience. This type of speech often includes the following elements: appeal to the needs of the audience. appeal to the reasoning of the audience. focus on the relevance of your topic to the audience.
The different types of public speaking are: Speaking to Inform (informative, argumentative speech) Speaking to Persuade, Motivate, or Take Action (persuasive, argumentative, controversial, policy speeches) Speaking to Entertain (funny, special occasion speeches) In this article: The 3 Basic Types of Public Speaking.
A part of speech (also called a word class) is a category that describes the role a word plays in a sentence.Understanding the different parts of speech can help you analyze how words function in a sentence and improve your writing. The parts of speech are classified differently in different grammars, but most traditional grammars list eight parts of speech in English: nouns, pronouns, verbs ...
Some figures of speech, like metaphor, simile, and metonymy, are found in everyday language. Others, like antithesis, circumlocution, and puns take more practice to implement in writing. Below are some common figures of speech with examples, so you can recognize them and use them in your writing. Give your writing extra polish.
2. Presentation Speech. These speeches are usually given at award ceremonies, where an individual presents an award or prize to a person. The main purpose of the presentation speech is to provide recognition of the recipient's accomplishments. 3. Toast. A toast is a brief tribute to a particular person or an event.
Four Types of Speech Delivery There are four types of speeches that most speakers utilize in delivering a speech. 1. Extemporaneous speeches are speeches that are carefully prepared and practiced by the speaker before the actual speaking time. A speaker will utilize notes or an outline as a guide while they are delivering the speech.
8 Parts of Speech Definitions and Examples: 1. Nouns are words that are used to name people, places, animals, ideas and things. Nouns can be classified into two main categories: Common nouns and Proper nouns. Common nouns are generic like ball, car, stick, etc., and proper nouns are more specific like Charles, The White House, The Sun, etc.
Let's look at all these categories of parts of speech with examples: 1. Nouns. A name or title given to an object, person, group, or concept is known as a noun.It can either be the subject of a sentence (an individual who acts) or the object of the verb (receiver of the action).
Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. ... Three main model types ...