A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis
Junjie Pan, Xiang Yin, Zhiling Zhang, Shichao Liu, Yang Zhang, Zejun Ma, Yuxuan Wang
Abstract
In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components requires extensive efforts. In this paper, we proposed a unified sequence-to-sequence front-end model for Mandarin TTS that converts raw texts to linguistic features directly. Compared to the pipeline-based front-end, our unified front-end can achieve comparable performance in polyphone disambiguation and prosody word prediction, and improve intonation phrase prediction by 0.0738 in F1 score. We also implemented the unified front-end with Tacotron and WaveRNN to build a Mandarin TTS system. The synthesized speech by that got a comparable MOS (4.38) with the pipeline-based front-end (4.37) and close to human recordings (4.49).
BibTeX
@inproceedings{icassp2020_aunifiedsequence,
title = {A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis},
author = {Junjie Pan and Xiang Yin and Zhiling Zhang and Shichao Liu and Yang Zhang and Zejun Ma and Yuxuan Wang},
booktitle = {ICASSP 2020},
year = {2020}
}