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While neural-based text to speech (TTS) models can synthesize natural andintelligible voice, they usually require high-quality speech data, which iscostly to collect. In many scenarios, only noisy speech of a target speaker isavailable, which presents challenges for TTS model training for this speaker.Previous works usually address the challenge using two methods: 1) training theTTS model using the speech denoised with an enhancement model; 2) taking asingle noise embedding as input when training with noisy speech. However, theyusually cannot handle speech with real-world complicated noise such as thosewith high variations along time. In this paper, we develop DenoiSpeech, a TTSsystem that can synthesize clean speech for a speaker with noisy speech data.In DenoiSpeech, we handle real-world noisy speech by modeling the fine-grainedframe-level noise with a noise condition module, which is jointly trained withthe TTS model. Experimental results on real-world data show that DenoiSpeechoutperforms the previous two methods by 0.31 and 0.66 MOS respectively.

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