Abstract
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.
Abstract (translated)
生成型人工智能在各个领域表现出令人印象深刻的表现，其中语音合成是一个有趣的方向。随着扩散模型成为最流行的生成模型，许多工作都尝试了两种活跃的任务：文本到语音和语音增强。这项工作对音频扩散模型进行了调查，是现有调查的互补，它们要么缺乏基于扩散的语音合成的最新进展，要么突出了在多个领域应用扩散模型的整体情况。具体来说，这项工作首先简要介绍了音频和扩散模型的背景。至于文本到语音任务，我们根据采用扩散模型的阶段将方法分为三个类别：声学模型、语音合成器和端到端框架。此外，我们根据输入语音中某些信号的去除或添加将各种语音增强任务分类。这项工作还涵盖了实验结果和讨论的比较。
URL
https://arxiv.org/abs/2303.13336
