  In indoor scenes, reverberation is a crucial factor in degrading the
perceived quality and intelligibility of speech. In this work, we propose a
generative dereverberation method. Our approach is based on a probabilistic
model utilizing a recurrent variational auto-encoder (RVAE) network and the
convolutive transfer function (CTF) approximation. Different from most previous
approaches, the output of our RVAE serves as the prior of the clean speech. And
our target is the maximum a posteriori (MAP) estimation of clean speech, which
is achieved iteratively through the expectation maximization (EM) algorithm.
The proposed method integrates the capabilities of network-based speech prior
modelling and CTF-based observation modelling. Experiments on single-channel
speech dereverberation show that the proposed generative method noticeably
outperforms the advanced discriminative networks.
