# Mutual Information Maximization for Effective Lip Reading

arXiv: https://arxiv.org/abs/2003.06439 · HTML: https://mlsanity.com/paper/2003.06439/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 18 of 18)

- [Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring](https://mlsanity.com/paper/2303.08536/) (2023)
- [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 Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video](https://mlsanity.com/paper/2204.01265/) (2022)
- [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)
- [LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers](https://mlsanity.com/paper/2302.02141/) (2023)
- [Multi-Temporal Lip-Audio Memory for Visual Speech Recognition](https://mlsanity.com/paper/2305.04542/) (2023)
- [Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization](https://mlsanity.com/paper/2403.16071/) (2024)
- [Advances and Challenges in Deep Lip Reading](https://mlsanity.com/paper/2110.07879/) (2021)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading](https://mlsanity.com/paper/2404.11979/) (2024)
- [Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading](https://mlsanity.com/paper/2108.03543/) (2021)
- [Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models](https://mlsanity.com/paper/2207.05692/) (2022)
- [Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading](https://mlsanity.com/paper/2402.11520/) (2024)
- [TD3Net: A temporal densely connected multi-dilated convolutional network for lipreading](https://mlsanity.com/paper/2506.16073/) (2025)
- [Bootstrapping Audiovisual Speech Recognition in Zero-AV-Resource Scenarios](https://mlsanity.com/paper/2603.08249/) (2026)

## Abstract (arXiv)

Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be to capture the lip movement information and meanwhile to resist the noises resulted from the change of pose, lighting conditions, speaker's appearance and so on. Towards this target, we propose to introduce the mutual information constraints on both the local feature's level and the global sequence's level to enhance the relations of the features with the speech content. On the one hand, we constraint the features generated at each time step to enable them carry a strong relation with the speech content by imposing the local mutual information maximization constraint (LMIM), leading to improvements over the model's ability to discover fine-grained lip movements and the fine-grained differences among words with similar pronunciation, such as ``spend'' and ``spending''. On the other hand, we introduce the mutual information maximization constraint on the global sequence's level (GMIM), to make the model be able to pay more attention to discriminate key frames related with the speech content, and less to various noises appeared in the speaking process. By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading. To verify this method, we evaluate on two large-scale benchmark. We perform a detailed analysis and comparison on several aspects, including the comparison of the LMIM and GMIM with the baseline, the visualization of the learned representation and so on. The results not only prove the effectiveness of the proposed method but also report new state-of-the-art performance on both the two benchmarks.