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Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

2021-03-26 16:40:28
Isaac Elias, Heiga Zen, Jonathan Shen, Yu Zhang, Jia Ye, RJ Ryan, Yonghui Wu

Abstract

This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration signals. The duration model is based on a novel attention mechanism and an iterative reconstruction loss based on Soft Dynamic Time Warping, this model can learn token-frame alignments as well as token durations automatically. Experimental results show that Parallel Tacotron 2 outperforms baselines in subjective naturalness in several diverse multi speaker evaluations. Its duration control capability is also demonstrated.

Abstract (translated)

URL

https://arxiv.org/abs/2103.14574

PDF

https://arxiv.org/pdf/2103.14574.pdf


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