GLip: A Global-Local Integrated Progressive Framework for Robust Visual Speech Recognition

2025-09-19

AI Summary

Existing visual speech recognition (VSR) methods exhibit limited robustness against real-world visual challenges—including illumination variations, occlusions, motion blur, and head pose changes. To address this, we propose a global-local fusion progressive lip-reading framework: (1) a dual-path feature extraction network that jointly models global structural context and local discriminative lip-region features via spatiotemporal dynamic fusion; (2) a Context Enhancement Module (CEM) to strengthen cross-frame temporal modeling; and (3) a two-stage training strategy—first achieving coarse-grained phoneme alignment, then refining fine-grained visual-to-acoustic mapping. Our method achieves significant improvements over state-of-the-art approaches on the LRS2 and LRS3 benchmarks. Moreover, it demonstrates strong robustness and generalization on a newly constructed, highly challenging Mandarin dataset featuring diverse adverse visual conditions.

Technology Category

• Computer Vision: Language and Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Speech

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Visual speech recognition (VSR), also known as lip reading, is the task of recognizing speech from silent video. Despite significant advancements in VSR over recent decades, most existing methods pay limited attention to real-world visual challenges such as illumination variations, occlusions, blurring, and pose changes. To address these challenges, we propose GLip, a Global-Local Integrated Progressive framework designed for robust VSR. GLip is built upon two key insights: (i) learning an initial extit{coarse} alignment between visual features across varying conditions and corresponding speech content facilitates the subsequent learning of extit{precise} visual-to-speech mappings in challenging environments; (ii) under adverse conditions, certain local regions (e.g., non-occluded areas) often exhibit more discriminative cues for lip reading than global features. To this end, GLip introduces a dual-path feature extraction architecture that integrates both global and local features within a two-stage progressive learning framework. In the first stage, the model learns to align both global and local visual features with corresponding acoustic speech units using easily accessible audio-visual data, establishing a coarse yet semantically robust foundation. In the second stage, we introduce a Contextual Enhancement Module (CEM) to dynamically integrate local features with relevant global context across both spatial and temporal dimensions, refining the coarse representations into precise visual-speech mappings. Our framework uniquely exploits discriminative local regions through a progressive learning strategy, demonstrating enhanced robustness against various visual challenges and consistently outperforming existing methods on the LRS2 and LRS3 benchmarks. We further validate its effectiveness on a newly introduced challenging Mandarin dataset.

Problem

• Addressing robustness to real-world visual challenges in lip reading
• Integrating global and local features for improved speech recognition
• Overcoming illumination variations, occlusions, blurring, and pose changes

Innovation

• Global-local dual-path feature extraction architecture
• Two-stage progressive coarse-to-fine learning
• Contextual Enhancement Module for spatiotemporal integration