Robust Self-Supervised Audio-Visual Speech Recognition

• URL: http://arxiv.org/abs/2201.01763v1
• Date: Wed, 5 Jan 2022 18:50:50 GMT
• Title: Robust Self-Supervised Audio-Visual Speech Recognition
• Authors: Bowen Shi and Wei-Ning Hsu and Abdelrahman Mohamed
• Abstract summary: We present a self-supervised audio-visual speech recognition framework built upon Audio-Visual HuBERT (AV-HuBERT) On the largest available AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by 50% (28.0% vs. 14.1%) using less than 10% of labeled data. Our approach reduces the WER of an audio-based model by over 75% (25.8% vs. 5.8%) on average.
• Score: 29.526786921769613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to noise and helps the model focus on the desired speaker. However, previous AVSR work focused solely on the supervised learning setup; hence the progress was hindered by the amount of labeled data available. In this work, we present a self-supervised AVSR framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art audio-visual speech representation learning model. On the largest available AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by ~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in the presence of babble noise, while reducing the WER of an audio-based model by over 75% (25.8% vs. 5.8%) on average.

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