  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.
