Self-supervised learning for audio-visual speaker diarization

• URL: http://arxiv.org/abs/2002.05314v1
• Date: Thu, 13 Feb 2020 02:36:32 GMT
• Title: Self-supervised learning for audio-visual speaker diarization
• Authors: Yifan Ding, Yong Xu, Shi-Xiong Zhang, Yahuan Cong and Liqiang Wang
• Abstract summary: We propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort. We test them on a real-world human-computer interaction system and the results show our best model yields a remarkable gain of +8%F1-scoresas well as diarization error rate reduction.
• Score: 33.87232473483064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort. We improve the previous approaches by introducing two new loss functions: the dynamic triplet loss and the multinomial loss. We test them on a real-world human-computer interaction system and the results show our best model yields a remarkable gain of +8%F1-scoresas well as diarization error rate reduction. Finally, we introduce a new large scale audio-video corpus designed to fill the vacancy of audio-video datasets in Chinese.

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