We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech
(TTS) synthesis model that can produce high-quality speech. Benefiting from a
modularized training paradigm exploiting self-supervised speech
representations, MParrotTTS adapts to a new language with minimal supervised
data and generalizes to languages not seen while training the self-supervised
backbone. Moreover, without training on any bilingual or parallel examples,
MParrotTTS can transfer voices across languages while preserving the
speaker-specific characteristics, e.g., synthesizing fluent Hindi speech using
a French speaker's voice and accent. We present extensive results on six
languages in terms of speech naturalness and speaker similarity in parallel and
cross-lingual synthesis. The proposed model outperforms the state-of-the-art
multilingual TTS models and baselines, using only a small fraction of
supervised training data. Speech samples from our model can be found at
this https URL

我们提出了 MParrotTTS，它是一个统一的多语言、多说话者的文字转语音合成模型，能够生成高质量的语音。使用自我监督语音表示的模块化训练范式，MParrotTTS 能够以最少的监督数据适应新语言，并在训练自我监督骨干的同时，泛化到未见过的语言。此外，MParrotTTS 不需对任何双语或平行例子进行训练，即可跨语言转移语音并保留说话者的特点。在六种语言上，我们通过并行和跨语言合成的语音流畅度和说话者相似度方面的广泛结果，证明了所提出模型优于现有的最先进的多语言 TTS 模型和基线， 只使用少量的受控训练数据。可以在此 https URL 找到我们模型的语音样本。