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arXiv · 2012.09547

DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling

Chen Zhang·Yi Ren·Xu Tan·Jinglin Liu·Kejun Zhang·Tao Qin·Sheng Zhao·Tie-Yan Liu
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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.

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Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu. 2020-12-17. DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling. https://arxiv.org/abs/2012.09547

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