  In this paper, we present the speaker diarization system for the
Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) from team
DKU_DukeECE. As the highly overlapped speech exists in the dataset, we employ
an x-vector-based target-speaker voice activity detection (TS-VAD) to find the
overlap between speakers. For the single-channel scenario, we separately train
a model for each of the 8 channels and fuse the results. We also employ the
cross-channel self-attention to further improve the performance, where the
non-linear spatial correlations between different channels are learned and
fused. Experimental results on the evaluation set show that the single-channel
TS-VAD reduces the DER by over 75% from 12.68\% to 3.14%. The multi-channel
TS-VAD further reduces the DER by 28% and achieves a DER of 2.26%. Our final
submitted system achieves a DER of 2.98% on the AliMeeting test set, which
ranks 1st in the M2MET challenge.
