# SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization

arXiv: https://arxiv.org/abs/2406.12233 · HTML: https://mlsanity.com/paper/2406.12233/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [End-to-end Audio-visual Speech Recognition with Conformers](https://mlsanity.com/paper/2102.06657/) (2021)
- [Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels](https://mlsanity.com/paper/2303.14307/) (2023)
- [Lipreading using Temporal Convolutional Networks](https://mlsanity.com/paper/2001.08702/) (2020)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [Sub-word Level Lip Reading With Visual Attention](https://mlsanity.com/paper/2110.07603/) (2021)
- [Discriminative Multi-modality Speech Recognition](https://mlsanity.com/paper/2005.05592/) (2020)
- [LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild](https://mlsanity.com/paper/1810.06990/) (2018)
- [Jointly Learning Visual and Auditory Speech Representations from Raw Data](https://mlsanity.com/paper/2212.06246/) (2022)
- [Towards Practical Lipreading with Distilled and Efficient Models](https://mlsanity.com/paper/2007.06504/) (2020)
- [LiRA: Learning Visual Speech Representations from Audio through Self-supervision](https://mlsanity.com/paper/2106.09171/) (2021)
- [Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers](https://mlsanity.com/paper/1911.11502/) (2019)
- [Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading](https://mlsanity.com/paper/2204.01725/) (2022)
- [Audio-visual Recognition of Overlapped speech for the LRS2 dataset](https://mlsanity.com/paper/2001.01656/) (2020)
- [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)

## Built on by (most cited first, 14 of 14)

- [AlignVSR: Audio-Visual Cross-Modal Alignment for Visual Speech Recognition](https://mlsanity.com/paper/2410.16438/) (2024)
- [The Lipreading Gap: Do VSR Models Perceive Visual Speech Like Human Lipreaders?](https://mlsanity.com/paper/2606.07435/) (2026)
- [Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation](https://mlsanity.com/paper/2502.05758/) (2025)
- [SwinLip: An Efficient Visual Speech Encoder for Lip Reading Using Swin Transformer](https://mlsanity.com/paper/2505.04394/) (2025)
- [GLip: A Global-Local Integrated Progressive Framework for Robust Visual Speech Recognition](https://mlsanity.com/paper/2509.16031/) (2025)
- [Shushing! Let's Imagine an Authentic Speech from the Silent Video](https://mlsanity.com/paper/2503.14928/) (2025)
- [CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization](https://mlsanity.com/paper/2505.03186/) (2025)
- [Leveraging Large Language Models in Visual Speech Recognition: Model Scaling, Context-Aware Decoding, and Iterative Polishing](https://mlsanity.com/paper/2506.02012/) (2025)
- [Enhancing CTC-Based Visual Speech Recognition](https://mlsanity.com/paper/2409.07210/) (2024)
- [Head-Pose-Aware Visual Speech Recognition with FiLM Modulation](https://mlsanity.com/paper/2606.00751/) (2026)
- [When Speech Meets Lips: Interpretable Audio-Visual Synchronization for L2 Pronunciation Assessment](https://mlsanity.com/paper/2609.06788/) (2026)
- [AVSRBench: A Multi-Condition AVSR Benchmark](https://mlsanity.com/paper/2609.10366/) (2026)
- [Candor-LR: A Dyadic Conversational Dataset for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2609.10394/) (2026)
- [Pose Adaptive Dynamic FiLM Modulation for Visual Speech Recognition](https://mlsanity.com/paper/2609.29443/) (2026)

## Abstract (arXiv)

Visual Speech Recognition (VSR) stands at the intersection of computer vision and speech recognition, aiming to interpret spoken content from visual cues. A prominent challenge in VSR is the presence of homophenes-visually similar lip gestures that represent different phonemes. Prior approaches have sought to distinguish fine-grained visemes by aligning visual and auditory semantics, but often fell short of full synchronization. To address this, we present SyncVSR, an end-to-end learning framework that leverages quantized audio for frame-level crossmodal supervision. By integrating a projection layer that synchronizes visual representation with acoustic data, our encoder learns to generate discrete audio tokens from a video sequence in a non-autoregressive manner. SyncVSR shows versatility across tasks, languages, and modalities at the cost of a forward pass. Our empirical evaluations show that it not only achieves state-of-the-art results but also reduces data usage by up to ninefold.