Related papers: Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training

Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training

URL: http://arxiv.org/abs/2010.15366v3
Date: Sun, 22 Aug 2021 06:26:39 GMT
Title: Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Authors: Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu, Yi-Chen Chen, Gene-Ping Yang, Hung-yi Lee
Abstract summary: We propose to perform self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen.
Score: 58.30339239234169
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired. In this paper, we propose to perform self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen.

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