Papers › A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion

A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion

3 Nov 2021arXiv:2111.02392archive 2025-07-28

Benjamin van Niekerk, Marc-André Carbonneau, Julian Zaïdi, Mathew Baas, Hugo Seuté, Herman Kamper

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/. Code available at https://github.com/bshall/soft-vc/.

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