# Large-Scale Visual Speech Recognition

arXiv: https://arxiv.org/abs/1807.05162 · HTML: https://mlsanity.com/paper/1807.05162/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 30 of 57)

- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [End-to-end Audio-visual Speech Recognition with Conformers](https://mlsanity.com/paper/2102.06657/) (2021)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces](https://mlsanity.com/paper/2306.10799/) (2023)
- [Sub-word Level Lip Reading With Visual Attention](https://mlsanity.com/paper/2110.07603/) (2021)
- [Jointly Learning Visual and Auditory Speech Representations from Raw Data](https://mlsanity.com/paper/2212.06246/) (2022)
- [VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning](https://mlsanity.com/paper/2211.11275/) (2022)
- [Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video](https://mlsanity.com/paper/2201.10439/) (2022)
- [Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing](https://mlsanity.com/paper/2402.15151/) (2024)
- [End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks](https://mlsanity.com/paper/2104.13332/) (2021)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [TVLT: Textless Vision-Language Transformer](https://mlsanity.com/paper/2209.14156/) (2022)
- [Audio-Visual Efficient Conformer for Robust Speech Recognition](https://mlsanity.com/paper/2301.01456/) (2023)
- [Deep Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision](https://mlsanity.com/paper/2303.17200/) (2023)
- [BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition](https://mlsanity.com/paper/2404.02098/) (2024)
- [AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model](https://mlsanity.com/paper/2308.07593/) (2023)
- [Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs](https://mlsanity.com/paper/2411.02256/) (2024)
- [More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech](https://mlsanity.com/paper/2111.10139/) (2021)
- [Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping](https://mlsanity.com/paper/2308.06112/) (2023)
- [Visual Keyword Spotting with Attention](https://mlsanity.com/paper/2110.15957/) (2021)
- [VALLR: Visual ASR Language Model for Lip Reading](https://mlsanity.com/paper/2503.21408/) (2025)
- [LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading](https://mlsanity.com/paper/2112.04748/) (2021)
- [Conformers are All You Need for Visual Speech Recognition](https://mlsanity.com/paper/2302.10915/) (2023)
- [MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation](https://mlsanity.com/paper/2303.09797/) (2023)
- [SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.09759/) (2024)
- [LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data](https://mlsanity.com/paper/2312.09727/) (2023)
- [Audio-Visual Speech Recognition is Worth 32×32×8 Voxels](https://mlsanity.com/paper/2109.09536/) (2021)
- [AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2309.17395/) (2023)
- [CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge](https://mlsanity.com/paper/2406.10313/) (2024)

## Abstract (arXiv)

This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.