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.

翻译：近年来，基于条件分数的扩散模型在有监督语音增强领域备受关注，取得了最先进的性能。然而，这些方法在面对未知条件时可能面临泛化挑战。为解决该问题，我们提出一种以无监督方式运作的替代方法，利用扩散模型的生成能力。具体而言，在训练阶段，通过基于分数的扩散模型在短时傅里叶变换域学习干净的语音先验分布，使其能够从高斯噪声无条件生成干净语音。随后，我们开发了一种后验采样方法，通过将学习的干净语音先验与噪声模型相结合，进行语音信号推断。噪声参数与干净语音估计通过迭代期望最大化方法同时学习。据我们所知，这是首次探索基于扩散生成模型的无监督语音增强研究，与近期基于变分自编码器的无监督方法及最先进的基于扩散的有监督方法相比，展现出有竞争力的结果。因此，该工作为无监督语音增强的未来研究开辟了新的方向。