UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder

• URL: http://arxiv.org/abs/2206.02512v2
• Date: Tue, 7 Jun 2022 01:30:17 GMT
• Title: UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder
• Authors: Jiachen Lian and Chunlei Zhang and Gopala Krishna Anumanchipalli and Dong Yu
• Abstract summary: We propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM) The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations.
• Score: 30.376259456529368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi-speaker speech synthesizer developed from the perspective of disentangled speech representation learning. The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. We leverage recent advancements in self-supervised speech representation learning as well as speech synthesis front-end techniques for the system development. Specifically, we utilize a lexicon to map input text to the phoneme sequence, which is expanded to the frame-level forced alignment (FA) with a speaker-dependent duration model. Then, we develop an alignment mapping module that converts the FA to the unsupervised alignment (UA). Finally, a Conditional Disentangled Sequential Variational Auto-encoder (C-DSVAE), serving as the self-supervised TTS AM, takes the predicted UA and a target speaker embedding to generate the mel spectrogram, which is ultimately converted to waveform with a neural vocoder. We show how our method enables speech synthesis without using a paired TTS corpus. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations.

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