Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module

Adam Gabryś; Goeric Huybrechts; Manuel Sam Ribeiro; Chung-Ming Chien; Julian Roth; Giulia Comini; Roberto Barra-Chicote; Bartek Perz; Jaime Lorenzo-Trueba

arXiv · 2202.08164

Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module

Adam Gabryś·Goeric Huybrechts·Manuel Sam Ribeiro·Chung-Ming Chien·Julian Roth·Giulia Comini·Roberto Barra-Chicote·Bartek Perz·Jaime Lorenzo-Trueba

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Abstract

State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data, standard TTS models suffer from speech quality and intelligibility degradations, making training low-resource TTS systems problematic. In this paper, we propose a novel extremely low-resource TTS method called Voice Filter that uses as little as one minute of speech from a target speaker. It uses voice conversion (VC) as a post-processing module appended to a pre-existing high-quality TTS system and marks a conceptual shift in the existing TTS paradigm, framing the few-shot TTS problem as a VC task. Furthermore, we propose to use a duration-controllable TTS system to create a parallel speech corpus to facilitate the VC task. Results show that the Voice Filter outperforms state-of-the-art few-shot speech synthesis techniques in terms of objective and subjective metrics on one minute of speech on a diverse set of voices, while being competitive against a TTS model built on 30 times more data.

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BibTeX RIS

Adam Gabryś, Goeric Huybrechts, Manuel Sam Ribeiro, Chung-Ming Chien, Julian Roth, Giulia Comini, Roberto Barra-Chicote, Bartek Perz, Jaime Lorenzo-Trueba. 2022-02-16. Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module. https://arxiv.org/abs/2202.08164

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