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.

翻译：生成式AI已在多个领域展现出令人瞩目的性能，其中语音合成是一个有趣的方向。以最流行的生成模型——扩散模型为基础，众多研究工作已尝试了两项活跃任务：文本转语音和语音增强。本文对音频扩散模型进行了综述，以补充现有综述中要么缺乏基于扩散模型的语音合成最新进展，要么侧重概述扩散模型在多个领域应用全貌的不足。具体而言，本文首先简要介绍音频和扩散模型的背景。针对文本转语音任务，我们根据扩散模型采用的阶段将方法分为三类：声学模型、声码器和端到端框架。此外，我们根据输入语音中是否移除或添加特定信号，对各类语音增强任务进行了分类。本综述还涵盖了实验结果的比较与讨论。