  This paper describes the Royalflush speaker diarization system submitted to
the Multi-channel Multi-party Meeting Transcription Challenge(M2MeT). Our
system comprises speech enhancement, overlapped speech detection, speaker
embedding extraction, speaker clustering, speech separation and system fusion.
In this system, we made three contributions. First, we propose an architecture
of combining the multi-channel and U-Net-based models, aiming at utilizing the
benefits of these two individual architectures, for far-field overlapped speech
detection. Second, in order to use overlapped speech detection model to help
speaker diarization, a speech separation based overlapped speech handling
approach, in which the speaker verification technique is further applied, is
proposed. Third, we explore three speaker embedding methods, and obtained the
state-of-the-art performance on the CNCeleb-E test set. With these proposals,
our best individual system significantly reduces DER from 15.25% to 6.40%, and
the fusion of four systems finally achieves a DER of 6.30% on the far-field
Alimeeting evaluation set.
