Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT

• URL: http://arxiv.org/abs/2205.07180v1
• Date: Sun, 15 May 2022 04:48:41 GMT
• Title: Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT
• Authors: Bowen Shi and Abdelrahman Mohamed and Wei-Ning Hsu
• Abstract summary: This paper investigates self-supervised pre-training for audio-visual speaker representation learning. A visual stream showing the speaker's mouth area is used alongside speech as inputs. We conducted extensive experiments probing the effectiveness of pre-training and visual modality.
• Score: 37.343431783936126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates self-supervised pre-training for audio-visual speaker representation learning where a visual stream showing the speaker's mouth area is used alongside speech as inputs. Our study focuses on the Audio-Visual Hidden Unit BERT (AV-HuBERT) approach, a recently developed general-purpose audio-visual speech pre-training framework. We conducted extensive experiments probing the effectiveness of pre-training and visual modality. Experimental results suggest that AV-HuBERT generalizes decently to speaker related downstream tasks, improving label efficiency by roughly ten fold for both audio-only and audio-visual speaker verification. We also show that incorporating visual information, even just the lip area, greatly improves the performance and noise robustness, reducing EER by 38% in the clean condition and 75% in noisy conditions. Our code and models will be publicly available.

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