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arXiv · 2312.14398

ZMM-TTS: Zero-shot Multilingual and Multispeaker Speech Synthesis Conditioned on Self-supervised Discrete Speech Representations

Cheng Gong·Xin Wang·Erica Cooper·Dan Wells·Longbiao Wang·Jianwu Dang·Korin Richmond·Junichi Yamagishi
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Abstract

Neural text-to-speech (TTS) has achieved human-like synthetic speech for single-speaker, single-language synthesis. Multilingual TTS systems are limited to resource-rich languages due to the lack of large paired text and studio-quality audio data. TTS systems are typically built using a single speaker's voices, but there is growing interest in developing systems that can synthesize voices for new speakers using only a few seconds of their speech. This paper presents ZMM-TTS, a multilingual and multispeaker framework utilizing quantized latent speech representations from a large-scale, pre-trained, self-supervised model. Our paper combines text-based and speech-based self-supervised learning models for multilingual speech synthesis. Our proposed model has zero-shot generalization ability not only for unseen speakers but also for unseen languages. We have conducted comprehensive subjective and objective evaluations through a series of experiments. Our model has proven effective in terms of speech naturalness and similarity for both seen and unseen speakers in six high-resource languages. We also tested the efficiency of our method on two hypothetically low-resource languages. The results are promising, indicating that our proposed approach can synthesize audio that is intelligible and has a high degree of similarity to the target speaker's voice, even without any training data for the new, unseen language.

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Cheng Gong, Xin Wang, Erica Cooper, Dan Wells, Longbiao Wang, Jianwu Dang, Korin Richmond, Junichi Yamagishi. 2023-12-22. ZMM-TTS: Zero-shot Multilingual and Multispeaker Speech Synthesis Conditioned on Self-supervised Discrete Speech Representations. https://arxiv.org/abs/2312.14398

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