# Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition

arXiv: https://arxiv.org/abs/2407.03563 · HTML: https://mlsanity.com/paper/2407.03563/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [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)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (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)
- [Robust Self-Supervised Audio-Visual Speech Recognition](https://mlsanity.com/paper/2201.01763/) (2022)
- [End-to-end Audiovisual Speech Recognition](https://mlsanity.com/paper/1802.06424/) (2018)
- [Discriminative Multi-modality Speech Recognition](https://mlsanity.com/paper/2005.05592/) (2020)
- [Jointly Learning Visual and Auditory Speech Representations from Raw Data](https://mlsanity.com/paper/2212.06246/) (2022)
- [u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality](https://mlsanity.com/paper/2207.07036/) (2022)
- [Deep Multimodal Learning for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1501.05396/) (2015)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition](https://mlsanity.com/paper/2207.06020/) (2022)
- [Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning](https://mlsanity.com/paper/2212.05301/) (2022)
- [Audio-Visual Efficient Conformer for Robust Speech Recognition](https://mlsanity.com/paper/2301.01456/) (2023)
- [BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition](https://mlsanity.com/paper/2404.02098/) (2024)
- [Audio-Visual Contrastive Learning with Temporal Self-Supervision](https://mlsanity.com/paper/2302.07702/) (2023)
- [Time Is MattEr: Temporal Self-supervision for Video Transformers](https://mlsanity.com/paper/2207.09067/) (2022)
- [No More Shortcuts: Realizing the Potential of Temporal Self-Supervision](https://mlsanity.com/paper/2312.13008/) (2023)
- [Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2305.09212/) (2023)
- [MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.03424/) (2024)
- [deHuBERT: Disentangling Noise in a Self-supervised Model for Robust Speech Recognition](https://mlsanity.com/paper/2302.14597/) (2023)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)

## Built on by (most cited first, 7 of 7)

- [Large Language Models are Strong Audio-Visual Speech Recognition Learners](https://mlsanity.com/paper/2409.12319/) (2024)
- [Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation](https://mlsanity.com/paper/2504.18539/) (2025)
- [MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition](https://mlsanity.com/paper/2502.10447/) (2025)
- [HuBERT-VIC: Improving Noise-Robust Automatic Speech Recognition of Speech Foundation Model via Variance-Invariance-Covariance Regularization](https://mlsanity.com/paper/2508.12292/) (2025)
- [GLip: A Global-Local Integrated Progressive Framework for Robust Visual Speech Recognition](https://mlsanity.com/paper/2509.16031/) (2025)
- [Noise-Robust AV-ASR Using Visual Features Both in the Whisper Encoder and Decoder](https://mlsanity.com/paper/2601.18396/) (2026)
- [Attention-Guided Reliability Scaling for Contrastive Decoding in Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2608.26213/) (2026)

## Abstract (arXiv)

Audio-visual speech recognition (AVSR) aims to transcribe human speech using both audio and video modalities. In practical environments with noise-corrupted audio, the role of video information becomes crucial. However, prior works have primarily focused on enhancing audio features in AVSR, overlooking the importance of video features. In this study, we strengthen the video features by learning three temporal dynamics in video data: context order, playback direction, and the speed of video frames. Cross-modal attention modules are introduced to enrich video features with audio information so that speech variability can be taken into account when training on the video temporal dynamics. Based on our approach, we achieve the state-of-the-art performance on the LRS2 and LRS3 AVSR benchmarks for the noise-dominant settings. Our approach excels in scenarios especially for babble and speech noise, indicating the ability to distinguish the speech signal that should be recognized from lip movements in the video modality. We support the validity of our methodology by offering the ablation experiments for the temporal dynamics losses and the cross-modal attention architecture design.