Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation

Jing-Xuan Zhang; Genshun Wan; Zhen-Hua Ling; Jia Pan; Jianqing Gao; Cong Liu

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arXiv · 2212.02782

Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation

Jing-Xuan Zhang·Genshun Wan·Zhen-Hua Ling·Jia Pan·Jianqing Gao·Cong Liu

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Abstract

In this work, we present a novel method, named AV2vec, for learning audio-visual speech representations by multimodal self-distillation. AV2vec has a student and a teacher module, in which the student performs a masked latent feature regression task using the multimodal target features generated online by the teacher. The parameters of the teacher model are a momentum update of the student. Since our target features are generated online, AV2vec needs no iteration step like AV-HuBERT and the total training time cost is reduced to less than one-fifth. We further propose AV2vec-MLM in this study, which augments AV2vec with a masked language model (MLM)-style loss using multitask learning. Our experimental results show that AV2vec achieved comparable performance to the AV-HuBERT baseline. When combined with an MLM-style loss, AV2vec-MLM outperformed baselines and achieved the best performance on the downstream tasks.

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BibTeX RIS

Jing-Xuan Zhang, Genshun Wan, Zhen-Hua Ling, Jia Pan, Jianqing Gao, Cong Liu. 2022-12-06. Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation. https://arxiv.org/abs/2212.02782

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