Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?

• URL: http://arxiv.org/abs/2204.12765v1
• Date: Wed, 27 Apr 2022 08:35:57 GMT
• Title: Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?
• Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Zhuo Chen, Peidong Wang, Gang Liu, Jinyu Li, Jian Wu, Xiangzhan Yu, Furu Wei
• Abstract summary: We study which factor leads to the success of self-supervised learning on speaker-related tasks. Our empirical results on the Voxceleb-1 dataset suggest that the benefit of SSL to SV task is from a combination of mask speech prediction loss, data scale, and model size.
• Score: 86.53044183309824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, self-supervised learning (SSL) has demonstrated strong performance in speaker recognition, even if the pre-training objective is designed for speech recognition. In this paper, we study which factor leads to the success of self-supervised learning on speaker-related tasks, e.g. speaker verification (SV), through a series of carefully designed experiments. Our empirical results on the Voxceleb-1 dataset suggest that the benefit of SSL to SV task is from a combination of mask speech prediction loss, data scale, and model size, while the SSL quantizer has a minor impact. We further employ the integrated gradients attribution method and loss landscape visualization to understand the effectiveness of self-supervised learning for speaker recognition performance.

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