The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models

Yi Wang; Oli Danyi Liu; Peter Bell

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arXiv · 2506.20361

The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models

Yi Wang·Oli Danyi Liu·Peter Bell

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Abstract

Human speech perception is multimodal. In natural speech, lip movements can precede corresponding voicing by a non-negligible gap of 100-300 ms, especially for specific consonants, affecting the time course of neural phonetic encoding in human listeners. However, it remains unexplored whether self-supervised learning models, which have been used to simulate audio-visual integration in humans, can capture this asynchronicity between audio and visual cues. We compared AV-HuBERT, an audio-visual model, with audio-only HuBERT, by using linear classifiers to track their phonetic decodability over time. We found that phoneme information becomes available in AV-HuBERT embeddings only about 20 ms before HuBERT, likely due to AV-HuBERT's lower temporal resolution and feature concatenation process. It suggests AV-HuBERT does not adequately capture the temporal dynamics of multimodal speech perception, limiting its suitability for modeling the multimodal speech perception process.

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BibTeX RIS

Yi Wang, Oli Danyi Liu, Peter Bell. 2025-06-25. The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models. https://arxiv.org/abs/2506.20361

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