  The paper introduces Diff-Filter, a multichannel speech enhancement approach
based on the diffusion probabilistic model, for improving speaker verification
performance under noisy and reverberant conditions. It also presents a new
two-step training procedure that takes the benefit of self-supervised learning.
In the first stage, the Diff-Filter is trained by conducting timedomain speech
filtering using a scoring-based diffusion model. In the second stage, the
Diff-Filter is jointly optimized with a pre-trained ECAPA-TDNN speaker
verification model under a self-supervised learning framework. We present a
novel loss based on equal error rate. This loss is used to conduct
selfsupervised learning on a dataset that is not labelled in terms of speakers.
The proposed approach is evaluated on MultiSV, a multichannel speaker
verification dataset, and shows significant improvements in performance under
noisy multichannel conditions.
