Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition

• URL: http://arxiv.org/abs/2203.07996v1
• Date: Thu, 24 Feb 2022 15:12:17 GMT
• Title: Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
• Authors: Xichen Pan, Peiyu Chen, Yichen Gong, Helong Zhou, Xinbing Wang, Zhouhan Lin
• Abstract summary: We leverage uni-modal self-supervised learning to promote the multimodal audio-visual speech recognition (AVSR) In particular, we first train audio and visual encoders on a large-scale uni-modal dataset, then we integrate components of both encoders into a larger multimodal framework. Our model is experimentally validated on both word-level and sentence-level AVSR tasks.
• Score: 23.239078852797817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training Transformer-based models demands a large amount of data, while obtaining parallel aligned and labelled data in multimodality is rather cost-demanding, especially for audio-visual speech recognition (AVSR). Thus it makes a lot of sense to make use of unlabelled uni-modal data. On the other side, although the effectiveness of large-scale self-supervised learning is well established in both audio and visual modalities, how to integrate those pre-trained models into a multimodal scenario remains underexplored. In this work, we successfully leverage uni-modal self-supervised learning to promote the multimodal AVSR. In particular, we first train audio and visual encoders on a large-scale uni-modal dataset, then we integrate components of both encoders into a larger multimodal framework which learns to recognize paired audio-visual data into characters through a combination of CTC and seq2seq decoding. We show that both components inherited from uni-modal self-supervised learning cooperate well, resulting in that the multimodal framework yields competitive results through fine-tuning. Our model is experimentally validated on both word-level and sentence-level AVSR tasks. Especially, even without an external language model, our proposed model raises the state-of-the-art performances on the widely accepted Lip Reading Sentences 2 (LRS2) dataset by a large margin, with a relative improvement of 30%.

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