This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional manner, it can be adapted to different tasks including degradation inversion, neural vocoding, and source separation. In this paper, we, first, tackle the challenging problem of unconditional waveform generation by comparing different neural architectures and preconditioning domains. After that, we demonstrate how the trained unconditional diffusion could be adapted to different tasks of speech processing by the means of recent developments in post-training conditioning of diffusion models. Finally, we demonstrate the performance of the proposed technique on the tasks of bandwidth extension, declipping, vocoding, and speech source separation and compare it to the baselines. The codes are publicly available.

翻译：本文提出UnDiff——一种能够解决多种语音逆问题的扩散概率模型。该模型以无条件方式训练用于语音波形生成后，可适配至降质逆变换、神经声码器及源分离等不同任务。首先，通过对比不同神经架构与预条件域，我们攻克了无条件波形生成这一挑战性问题；其次，基于扩散模型训练后条件处理的最新进展，展示了已训练的无条件扩散模型如何适配至不同语音处理任务；最后，在带宽扩展、去削波、声码化及语音源分离任务上验证了所提技术的性能，并与基线方法进行对比。相关代码已公开。