A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)(2022)
摘要
The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning for voice conversion. Specifically, we compare discrete and soft speech units as input features. We find that discrete representations effectively remove speaker information but discard some linguistic content - leading to mispronunciations. As a solution, we propose soft speech units. To learn soft units, we predict a distribution over discrete speech units. By modeling uncertainty, soft units capture more content information, improving the intelligibility and naturalness of converted speech. Samples available at https://ubisoft-laforge.github.io/speech/soft-vc/
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关键词
voice conversion,speech synthesis,self-supervised learning,acoustic unit discovery
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