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Speech Processing
SPE-P12.2
Poster
Machine Learning for Speech Synthesis II

FLOW-TTS: A NON-AUTOREGRESSIVE NETWORK FOR TEXT TO SPEECH BASED ON FLOW

Chenfeng Miao

Date & Time

Thu, May 7, 2020

12:30 pm – 2:30 pm

Location

On-Demand

Abstract

In this work, we propose Flow-TTS, a non-autoregressive end-to-end neural TTS model based on generative flow. Unlike other non-autoregressive models, Flow-TTS can achieve high-quality speech generation by using a single feed-forward network. To our knowledge, Flow-TTS is the first TTS model utilizing flow in spectrogram generation network and the first non-autoregssive model which jointly learns the alignment and spectrogram generation through a single network. Experiments on LJSpeech show that the speech quality of Flow-TTS heavily approaches that of human and is even better than that of autoregressive model Tacotron 2 (outperforms Tacotron 2 with a gap of 0.09 in MOS). Meanwhile, the inference speed of Flow-TTS is about 23 times speed-up over Tacotron 2, which is comparable to FastSpeech.


Presenter

Chenfeng Miao

Ping An Technology (Shenzhen) Co., Ltd.
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Session Chairs

Tomoki Toda

Nagoya University

Zhiyong Wu

Tsinghua University