visual speech recognition for multiple languages in the wild

The authors propose a VSR model with auxiliary tasks, hyperparameter optimization and data augmentation, and show that it outperforms previous methods on publicly available datasets. The paper is published in Nature Machine Intelligence and available on arXiv.

Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large...

This is the repository of Visual Speech Recognition for Multiple Languages, which is the successor of End-to-End Audio-Visual Speech Recognition with Conformers. By using this repository, you can achieve the performance of 19.1%, 1.0% and 0.9% WER for automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR) on LRS3.

A novel method for VSR that outperforms previous methods trained on larger datasets. The model uses auxiliary tasks, data augmentations and hyperparameter optimization to achieve state-of-the-art performance in English, Mandarin and Spanish.

Abstract| Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led to the development of much more accurate and robust VSR models than ever before. However, these advances are

Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led to the development of much more accurate and robust VSR models than ever before.

Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large...

We provide state-of-the-art algorithms for end-to-end visual speech recognition in the wild. The repository is composed of face tracking, pre-processing, and acoustic/visual encoder backbones. Our models provide state-of-the-art performance for speech recognition datasets.

• We propose a novel method for visual speech recog-nition, which outperforms state-of-the-art methods trained on publicly available data by a large margin. • We do so by a VSR model with auxiliary tasks that jointly performs visual speech recognition and predic-tion of audio and visual representations.

Visual speech recognition (VSR) aims to recognise the content of speech based on the lip movements without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led to the development of much more accurate and robust VSR models than ever before.