  Speaker verification (SV) suffers from unsatisfactory performance in
far-field scenarios due to environmental noise andthe adverse impact of room
reverberation. This work presents a benchmark of multichannel speech
enhancement for far-fieldspeaker verification. One approach is a deep neural
network-based, and the other is a combination of deep neural network andsignal
processing. We integrated a DNN architecture with signal processing techniques
to carry out various experiments. Ourapproach is compared to the existing
state-of-the-art approaches. We examine the importance of enrollment in
pre-processing,which has been largely overlooked in previous studies.
Experimental evaluation shows that pre-processing can improve the SVperformance
as long as the enrollment files are processed similarly to the test data and
that test and enrollment occur within similarSNR ranges. Considerable
improvement is obtained on the generated and all the noise conditions of the
VOiCES dataset.
