End-to-End Text-to-Speech using Latent Duration based on VQ-VAE

• URL: http://arxiv.org/abs/2010.09602v2
• Date: Tue, 20 Oct 2020 13:46:35 GMT
• Title: End-to-End Text-to-Speech using Latent Duration based on VQ-VAE
• Authors: Yusuke Yasuda, Xin Wang, Junichi Yamagishi
• Abstract summary: Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS) We propose a new TTS framework using explicit duration modeling that incorporates duration as a discrete latent variable to TTS.
• Score: 48.151894340550385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS). We propose a new TTS framework using explicit duration modeling that incorporates duration as a discrete latent variable to TTS and enables joint optimization of whole modules from scratch. We formulate our method based on conditional VQ-VAE to handle discrete duration in a variational autoencoder and provide a theoretical explanation to justify our method. In our framework, a connectionist temporal classification (CTC) -based force aligner acts as the approximate posterior, and text-to-duration works as the prior in the variational autoencoder. We evaluated our proposed method with a listening test and compared it with other TTS methods based on soft-attention or explicit duration modeling. The results showed that our systems rated between soft-attention-based methods (Transformer-TTS, Tacotron2) and explicit duration modeling-based methods (Fastspeech).

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