How do self-supervised audio-visual representations perform in low-resource settings compared to traditional a

Abstract

Abstract: The intuitive interaction between the audio and visual modalities is valuable for cross-modal self-supervised learning. This concept has been demonstrated for generic audiovisual tasks like video action recognition and acoustic scene classification. However, self-supervision remains under-explored for audiovisual speech. We propose a method to learn self-supervised speech representations from the raw audio waveform. We train a raw audio encoder by combining audio-only self-supervision (by predicting informative audio attributes) with visual self-supervision (by generating talking faces from au

Research Question

How do self-supervised audio-visual representations perform in low-resource settings compared to traditional audio-only models on the LipNet or LRW benchmarks in terms of word error rate (WER) under visual occlusion conditions?

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