ParrotTTS：利用自监督表征的文本到语音合成
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations
AI总结：
本文提出模块化 TTS 模型 ParrotTTS，利用解耦的自监督语音表征，实现单说话人转写训练多说话人模型、低资源语言适配、跨语言声音迁移，并以少量配对数据超越现有多语 TTS。
摘要：
我们提出了 ParrotTTS，这是一个模块化的文本到语音合成模型，利用了解耦的自监督语音表征。它能够仅使用来自单一说话人的转写文本，有效地训练多说话人变体。ParrotTTS 可在低资源场景下适配新语言，并能泛化到训练自监督骨干网络时未见过的语言。此外，无需在双语或平行样本上进行训练，ParrotTTS 即可跨语言迁移声音，同时保留说话人特有的特征，例如用法语说话人的声音和口音合成流利的印地语语音。我们在单语和多语场景中给出了大量实验结果。ParrotTTS 仅使用最先进多语 TTS 模型所需配对数据的一小部分，性能便优于这些模型。
英文摘要：
We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations. It can train a multi-speaker variant effectively using transcripts from a single speaker. ParrotTTS adapts to a new language in low resource setup and generalizes to languages not seen while training the self-supervised backbone. Moreover, without training on bilingual or parallel examples, ParrotTTS 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 in monolingual and multi-lingual scenarios. ParrotTTS outperforms state-of-the-art multi-lingual TTS models using only a fraction of paired data as latter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
