ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations

• URL: http://arxiv.org/abs/2303.01261v3
• Date: Sun, 17 Dec 2023 00:06:16 GMT
• Title: ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations
• Authors: Neil Shah, Saiteja Kosgi, Vishal Tambrahalli, Neha Sahipjohn, Niranjan Pedanekar, Vineet Gandhi
• Abstract summary: ParrotTTS is a modularized text-to-speech synthesis model. It can train a multi-speaker variant effectively using transcripts from a single speaker. It adapts to a new language in low resource setup and generalizes to languages not seen while training the self-supervised backbone.
• Score: 27.157701195636477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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