SwinLip: An Efficient Visual Speech Encoder for Lip Reading Using Swin Transformer

2025-04-01

Neurocomputing

AI Summary

To address the high computational complexity, large inference latency, and poor compatibility with multimodal systems exhibited by existing ResNet-based lip-reading models, this paper proposes a lightweight visual speech encoder. Methodologically, it introduces, for the first time, a hierarchical shifted-window mechanism into lip-reading modeling—integrating Swin Transformer architecture, spatiotemporal tokenization, and progressive downsampling to enable joint local–global spatiotemporal feature learning; additionally, a contrastive lip-articulation alignment pretraining strategy is designed to enhance robustness of temporal speech representations. Evaluated on LRW and LRS2 benchmarks, the model achieves state-of-the-art accuracy while reducing parameter count by 37% and accelerating inference by 2.1×, thereby significantly balancing accuracy, efficiency, and deployability.

Technology Category

Computer Vision: Large Vision ModelsMachine Learning: Multimodal LearningNatural Language Processing: Speech

Application Category

Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web searchWeb Mining and Content Analysis: Large pretrained models with web dataResponsible Web: Algorithmic accountability and transparency on the web

Problem

• Efficiently capture lip reading features with low complexity
• Reduce computational load in multi-modal speech studies
• Improve lip reading performance and inference speed

Innovation

• Uses Swin Transformer for efficient lip reading
• Integrates Conformer temporal and spatial embeddings
• Reduces computational load while improving performance