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 will be released soon.

本文介绍了 UnDiff，一种扩散概率模型，能够解决各种语音反问题，并可以应用于条件不同的任务，如降级反演、神经声波编解码和语音来源分离等。首先，我们比较了不同的神经架构和预处理方法来解决无条件波形生成的难题，并通过最新的扩散模型后处理技术演示了如何将无条件扩散模型应用于语音处理中的不同任务。最终，在带宽扩展、去剪辑、声学编解码和语音来源分离等任务上展示了所提出的技术的性能，并与基线进行比较。