Architectural Modifications in M2S-AVSR for Low-Resource Audio-Visual Speech Recognition

Abstract

Abstract: Audio-Visual Speech Recognition (AVSR) enhances speech recognition robustness by leveraging visual cues, while real-world scenarios remain challenging due to viewpoint variation, audio distortion, and visual occlusion, which degrade modality quality and increase audio-visual asynchrony. In this paper, we propose a novel Modality-aware Multi-view Self-supervised representation framework for robust Audio-Visual Speech Recognition (M2S-AVSR). First, we introduce a multi-view representation learning encoder to learn view-invariant visual speech representations. Next, we employ a modality-aware mod

Research Question

What is the impact of architectural modifications in M2S-AVSR (e.g., attention mechanisms, fusion layers) on its scalability and accuracy in low-resource audio-visual speech recognition tasks, evaluated on AVSpeech and LRS3 benchmarks?

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