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AI在各个领域表现出令人印象深刻的性能,其中语音合成是一个有趣的方向。作为最流行的生成模型,扩散模型在文本转语音和语音增强这两个活跃领域中已有大量工作。本文对音频扩散模型进行了调查,补充了现有调查不足的部分,如缺乏基于扩散模型的最新语音合成进展或只强调应用扩散模型在多个领域的整体情况。具体而言,本文首先简要介绍了音频和扩散模型的背景。对于文本转语音任务,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,我们通过移除或添加到输入语音中某些信号,将各种语音增强任务进行分类。本文还涵盖了实验结果的比较和讨论。