Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios
Abstract
In the existing cross-speaker style transfer task, a source speaker with multi-style recordings is necessary to provide the style for a target speaker. However, it is hard for one speaker to express all expected styles. In this paper, a more general task, which is to produce expressive speech by combining any styles and timbres from a multi-speaker corpus in which each speaker has a unique style, is proposed. To realize this task, a novel method is proposed. This method is a Tacotron2-based framework but with a fine-grained text-based prosody predicting module and a speaker identity controller. Experiments demonstrate that the proposed method can successfully express a style of one speaker with the timber of another speaker bypassing the dependency on a single speaker's multi-style corpus. Moreover, the explicit prosody features used in the prosody predicting module can increase the diversity of synthetic speech by adjusting the value of prosody features.
Keywords
Cite
@article{arxiv.2112.12743,
title = {Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios},
author = {Qicong Xie and Tao Li and Xinsheng Wang and Zhichao Wang and Lei Xie and Guoqiao Yu and Guanglu Wan},
journal= {arXiv preprint arXiv:2112.12743},
year = {2021}
}
Comments
submitted to icassp2022