AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations

Jiachen Lian; Alexei Baevski; Wei-Ning Hsu; Michael Auli

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arXiv · 2302.06419

AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations

Jiachen Lian·Alexei Baevski·Wei-Ning Hsu·Michael Auli

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Abstract

Self-supervision has shown great potential for audio-visual speech recognition by vastly reducing the amount of labeled data required to build good systems. However, existing methods are either not entirely end-to-end or do not train joint representations of both modalities. In this paper, we introduce AV-data2vec which addresses these challenges and builds audio-visual representations based on predicting contextualized representations which has been successful in the uni-modal case. The model uses a shared transformer encoder for both audio and video and can combine both modalities to improve speech recognition. Results on LRS3 show that AV-data2vec consistently outperforms existing methods under all settings with the same amount of data and model size.

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BibTeX RIS

Jiachen Lian, Alexei Baevski, Wei-Ning Hsu, Michael Auli. 2023-02-10. AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations. https://arxiv.org/abs/2302.06419

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