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

• URL: http://arxiv.org/abs/2302.06419v2
• Date: Sun, 21 Jan 2024 07:41:02 GMT
• Title: AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations
• Authors: Jiachen Lian and Alexei Baevski and Wei-Ning Hsu and Michael Auli
• Abstract summary: We introduce AV-data2vec which builds audio-visual representations based on predicting contextualized representations. Results on LRS3 show that AV-data2vec consistently outperforms existing methods with the same amount of data and model size.
• Score: 88.30635799280923
• License: http://creativecommons.org/licenses/by/4.0/
• 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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