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arXiv · 2210.15936

A comprehensive study on self-supervised distillation for speaker representation learning

Zhengyang Chen·Yao Qian·Bing Han·Yanmin Qian·Michael Zeng
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Abstract

In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb1 speaker verification evaluation benchmark ( i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791% for trial Vox1-O, Vox1-E and Vox1-H , respectively), discarding any speaker labels in the training phase.

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Zhengyang Chen, Yao Qian, Bing Han, Yanmin Qian, Michael Zeng. 2022-11-25. A comprehensive study on self-supervised distillation for speaker representation learning. https://arxiv.org/abs/2210.15936

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