mlsanityData to 14 Oct 2026

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

arXiv 2205.07180 ↗First posted 15 May 2022eess.AS cs.CV cs.SDcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 33 of its 44 references that are in the map. First posted on arXiv 15 May 2022.

Builds on

33 of 44 references.

References · 33

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21 more
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 3.

8 papers.