Unsupervised speech enhancement with diffusion-based generative models

TL;DR

This paper introduces an unsupervised speech enhancement method using diffusion models that learns clean speech priors in the STFT domain and iteratively estimates noise and speech, showing promising results over existing approaches.

Contribution

It presents the first unsupervised diffusion-based speech enhancement framework that combines learned speech priors with noise modeling via EM, addressing generalization issues of supervised methods.

Findings

Outperforms recent VAE-based unsupervised methods.

Achieves competitive results compared to supervised diffusion models.

Demonstrates the effectiveness of diffusion models in unsupervised speech enhancement.

Abstract

Recently, conditional score-based diffusion models have gained significant attention in the field of supervised speech enhancement, yielding state-of-the-art performance. However, these methods may face challenges when generalising to unseen conditions. To address this issue, we introduce an alternative approach that operates in an unsupervised manner, leveraging the generative power of diffusion models. Specifically, in a training phase, a clean speech prior distribution is learnt in the short-time Fourier transform (STFT) domain using score-based diffusion models, allowing it to unconditionally generate clean speech from Gaussian noise. Then, we develop a posterior sampling methodology for speech enhancement by combining the learnt clean speech prior with a noise model for speech signal inference. The noise parameters are simultaneously learnt along with clean speech estimation through an iterative expectationmaximisation (EM) approach. To the best of our knowledge, this is the first work exploring diffusion-based generative models for unsupervised speech enhancement, demonstrating promising results compared to a recent variational auto-encoder (VAE)-based unsupervised approach and a state-of-the-art diffusion-based supervised method. It thus opens a new direction for future research in unsupervised speech enhancement.