# Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition

arXiv: https://arxiv.org/abs/2207.06020 · HTML: https://mlsanity.com/paper/2207.06020/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (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)
- [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)
- [Recurrent Neural Network Transducer for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1911.04890/) (2019)
- [End-to-end Audiovisual Speech Recognition](https://mlsanity.com/paper/1802.06424/) (2018)
- [Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture](https://mlsanity.com/paper/1810.00108/) (2018)
- [Discriminative Multi-modality Speech Recognition](https://mlsanity.com/paper/2005.05592/) (2020)
- [Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers](https://mlsanity.com/paper/1911.11502/) (2019)
- [Lip to Speech Synthesis with Visual Context Attentional GAN](https://mlsanity.com/paper/2204.01726/) (2022)
- [Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading](https://mlsanity.com/paper/2204.01725/) (2022)
- [Deep Multimodal Learning for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1501.05396/) (2015)
- [Audio-visual Recognition of Overlapped speech for the LRS2 dataset](https://mlsanity.com/paper/2001.01656/) (2020)
- [Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video](https://mlsanity.com/paper/2201.10439/) (2022)
- [Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video](https://mlsanity.com/paper/2204.01265/) (2022)
- [Visual Speech Enhancement Without A Real Visual Stream](https://mlsanity.com/paper/2012.10852/) (2020)

## Built on by (most cited first, 24 of 24)

- [Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring](https://mlsanity.com/paper/2303.08536/) (2023)
- [AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model](https://mlsanity.com/paper/2308.07593/) (2023)
- [Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge](https://mlsanity.com/paper/2308.09311/) (2023)
- [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://mlsanity.com/paper/2302.08102/) (2023)
- [AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation](https://mlsanity.com/paper/2312.02512/) (2023)
- [A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2403.04245/) (2024)
- [Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs](https://mlsanity.com/paper/2503.06362/) (2025)
- [Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach](https://mlsanity.com/paper/2505.14336/) (2025)
- [Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations](https://mlsanity.com/paper/2503.06273/) (2025)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)
- [Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2407.03563/) (2024)
- [Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation](https://mlsanity.com/paper/2504.18539/) (2025)
- [Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video](https://mlsanity.com/paper/2303.08670/) (2023)
- [Exploring Phonetic Context-Aware Lip-Sync For Talking Face Generation](https://mlsanity.com/paper/2305.19556/) (2023)
- [MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition](https://mlsanity.com/paper/2502.10447/) (2025)
- [MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2510.04136/) (2025)
- [Contextual Speech Extraction: Leveraging Textual History as an Implicit Cue for Target Speech Extraction](https://mlsanity.com/paper/2503.08798/) (2025)
- [Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment](https://mlsanity.com/paper/2501.18157/) (2025)
- [Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation Models](https://mlsanity.com/paper/2502.05766/) (2025)
- [Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses](https://mlsanity.com/paper/2510.13281/) (2025)
- [Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition](https://mlsanity.com/paper/2603.12046/) (2026)
- [Purification Before Fusion: Toward Mask-Free Speech Enhancement for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2601.12436/) (2026)
- [Materialistic RIR: Material Conditioned Realistic RIR Generation](https://mlsanity.com/paper/2604.21119/) (2026)
- [Attention-Guided Reliability Scaling for Contrastive Decoding in Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2608.26213/) (2026)

## Abstract (arXiv)

This paper focuses on designing a noise-robust end-to-end Audio-Visual Speech Recognition (AVSR) system. To this end, we propose Visual Context-driven Audio Feature Enhancement module (V-CAFE) to enhance the input noisy audio speech with a help of audio-visual correspondence. The proposed V-CAFE is designed to capture the transition of lip movements, namely visual context and to generate a noise reduction mask by considering the obtained visual context. Through context-dependent modeling, the ambiguity in viseme-to-phoneme mapping can be refined for mask generation. The noisy representations are masked out with the noise reduction mask resulting in enhanced audio features. The enhanced audio features are fused with the visual features and taken to an encoder-decoder model composed of Conformer and Transformer for speech recognition. We show the proposed end-to-end AVSR with the V-CAFE can further improve the noise-robustness of AVSR. The effectiveness of the proposed method is evaluated in noisy speech recognition and overlapped speech recognition experiments using the two largest audio-visual datasets, LRS2 and LRS3.