Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

• URL: http://arxiv.org/abs/2201.02184v1
• Date: Wed, 5 Jan 2022 17:40:45 GMT
• Title: Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
• Authors: Bowen Shi and Wei-Ning Hsu and Kushal Lakhotia and Abdelrahman Mohamed
• Abstract summary: Video recordings of speech contain correlated audio and visual information. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition.
• Score: 26.27172574676212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker's lip movements and the produced sound. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition. On the largest public lip-reading benchmark LRS3 (433 hours), AV-HuBERT achieves 32.5% WER with only 30 hours of labeled data, outperforming the former state-of-the-art approach (33.6%) trained with a thousand times more transcribed video data (31K hours). The lip-reading WER is further reduced to 26.9% when using all 433 hours of labeled data from LRS3 and combined with self-training. Using our audio-visual representation on the same benchmark for audio-only speech recognition leads to a 40% relative WER reduction over the state-of-the-art performance (1.3% vs 2.3%). Our code and models are available at https://github.com/facebookresearch/av_hubert

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