In this paper, we address the unsupervised speech enhancement problem based on recurrent variational autoencoder (RVAE). This approach offers promising generalization performance over the supervised counterpart. Nevertheless, the involved iterative variational expectation-maximization (VEM) process at test time, which relies on a variational inference method, results in high computational complexity. To tackle this issue, we present efficient sampling techniques based on Langevin dynamics and Metropolis-Hasting algorithms, adapted to the EM-based speech enhancement with RVAE. By directly sampling from the intractable posterior distribution within the EM process, we circumvent the intricacies of variational inference. We conduct a series of experiments, comparing the proposed methods with VEM and a state-of-the-art supervised speech enhancement approach based on diffusion models. The results reveal that our sampling-based algorithms significantly outperform VEM, not only in terms of computational efficiency but also in overall performance. Furthermore, when compared to the supervised baseline, our methods showcase robust generalization performance in mismatched test conditions.

翻译：本文针对基于循环变分自编码器(RVAE)的无监督语音增强问题展开研究。相比监督方法，该方案展现出更具前景的泛化性能。然而，测试阶段所涉及的迭代变分期望最大化(VEM)过程依赖于变分推断方法，导致计算复杂度较高。为解决这一问题，我们提出基于朗之万动力学和Metropolis-Hasting算法的高效采样技术，并将其适配至基于EM的RVAE语音增强框架。通过在EM过程中直接对难解后验分布进行采样，我们规避了变分推断的复杂性。我们开展了一系列实验，将所提方法与VEM方法及基于扩散模型的最新监督语音增强方法进行对比。结果表明，我们提出的基于采样的算法不仅在计算效率上显著优于VEM，在整体性能方面同样表现更佳。此外，与监督基线方法相比，我们的方法在失配测试条件下展现出稳健的泛化性能。