Generative AI has demonstrated impressive performance in various fields,
among which speech synthesis is an interesting direction. With the diffusion
model as the most popular generative model, numerous works have attempted two
active tasks: text to speech and speech enhancement. This work conducts a
survey on audio diffusion model, which is complementary to existing surveys
that either lack the recent progress of diffusion-based speech synthesis or
highlight an overall picture of applying diffusion model in multiple fields.
Specifically, this work first briefly introduces the background of audio and
diffusion model. As for the text-to-speech task, we divide the methods into
three categories based on the stage where diffusion model is adopted: acoustic
model, vocoder and end-to-end framework. Moreover, we categorize various speech
enhancement tasks by either certain signals are removed or added into the input
speech. Comparisons of experimental results and discussions are also covered in
this survey.

本文对音频扩散模型进行了调查，针对文本转语音和语音增强这两个活跃任务，它将方法分为三类，即声学模型、波导合成和端到端框架，并通过添加或去除特定信号将各种语音增强任务进行分类，最后进行实验结果比较和讨论。