  Motivated by unconsolidated data situation and the lack of a standard
benchmark in the field, we complement our previous efforts and present a
comprehensive corpus designed for training and evaluating text-independent
multi-channel speaker verification systems. It can be readily used also for
experiments with dereverberation, denoising, and speech enhancement. We tackled
the ever-present problem of the lack of multi-channel training data by
utilizing data simulation on top of clean parts of the Voxceleb dataset. The
development and evaluation trials are based on a retransmitted Voices Obscured
in Complex Environmental Settings (VOiCES) corpus, which we modified to provide
multi-channel trials. We publish full recipes that create the dataset from
public sources as the MultiSV corpus, and we provide results with two of our
multi-channel speaker verification systems with neural network-based
beamforming based either on predicting ideal binary masks or the more recent
Conv-TasNet.
