  To improve speaker verification in real scenarios with interference speakers,
noise, and reverberation, we propose to bring together advancements made in
multi-channel speech features. Specifically, we combine spectral, spatial, and
directional features, which includes inter-channel phase difference,
multi-channel sinc convolutions, directional power ratio features, and angle
features. To maximally leverage supervised learning, our framework is also
equipped with multi-channel speech enhancement and voice activity detection. On
all simulated, replayed, and real recordings, we observe large and consistent
improvements at various degradation levels. On real recordings of multi-talker
speech, we achieve a 36% relative reduction in equal error rate w.r.t.
single-channel baseline. We find the improvements from speaker-dependent
directional features more consistent in multi-talker conditions than clean.
Lastly, we investigate if the learned multi-channel speaker embedding space can
be made more discriminative through a contrastive loss-based fine-tuning. With
a simple choice of Triplet loss, we observe a further 8.3% relative reduction
in EER.
