  This paper describes a spatial-aware speaker diarization system for the
multi-channel multi-party meeting. The diarization system obtains direction
information of speaker by microphone array. Speaker spatial embedding is
generated by xvector and s-vector derived from superdirective beamforming (SDB)
which makes the embedding more robust. Specifically, we propose a novel
multi-channel sequence-to-sequence neural network architecture named
discriminative multi-stream neural network (DMSNet) which consists of attention
superdirective beamforming (ASDB) block and Conformer encoder. The proposed
ASDB is a self-adapted channel-wise block that extracts the latent spatial
features of array audios by modeling interdependencies between channels. We
explore DMSNet to address overlapped speech problem on multi-channel audio and
achieve 93.53% accuracy on evaluation set. By performing DMSNet based
overlapped speech detection (OSD) module, the diarization error rate (DER) of
cluster-based diarization system decrease significantly from 13.45% to 7.64%.
