  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.
