Paper Info
Title
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.(1)
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9413934
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
text to speech, speech synthesis, noisy speech, denoise, frame-level condition
Conference
0
PageRank 
References 
Authors
0.34
7
8
Name
Order
Citations
PageRank
Chen Zhang11255.22
Ren, Yi2104.35
Xu Tan38823.94
Jinglin Liu401.35
Kejun Zhang5276.35
Tao Qin62384147.25
Zhao, Sheng751.42
Tie-yan Liu84662256.32