DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling
Abstract
While neural-based text to speech (TTS) models can synthesize natural and intelligible voice, they usually require high-quality speech data, which is costly to collect. In many scenarios, only noisy speech of a target speaker is available, which presents challenges for TTS model training for this speaker. Previous works usually address the challenge using two methods: 1) training the TTS model using the speech denoised with an enhancement model; 2) taking a single noise embedding as input when training with noisy speech. However, they usually cannot handle speech with real-world complicated noise such as those with high variations along time. In this paper, we develop DenoiSpeech, a TTS system that can synthesize clean speech for a speaker with noisy speech data. In DenoiSpeech, we handle real-world noisy speech by modeling the fine-grained frame-level noise with a noise condition module, which is jointly trained with the TTS model. Experimental results on real-world data show that DenoiSpeech outperforms the previous two methods by 0.31 and 0.66 MOS respectively.
Cite
@article{arxiv.2012.09547,
title = {DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling},
author = {Chen Zhang and Yi Ren and Xu Tan and Jinglin Liu and Kejun Zhang and Tao Qin and Sheng Zhao and Tie-Yan Liu},
journal= {arXiv preprint arXiv:2012.09547},
year = {2020}
}