UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model

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.

Iterative autoregression: a novel trick to improve your low-latency speech enhancement model

Streaming models are an essential component of real-time speech enhancement tools. The streaming regime constrains speech enhancement models to use only a tiny context of future information, thus, the low-latency streaming setup is generally assumed to be challenging and has a significant negative effect on the model quality. However, due to the sequential nature of streaming generation, it provides a natural possibility for autoregression, i.e., using previous predictions when making current ones. In this paper, we present a simple, yet effective trick for training of autoregressive low-latency speech enhancement models. We demonstrate that the proposed technique leads to stable improvement across different architectures and training scenarios.