  We propose two improvements to target-speaker voice activity detection
(TS-VAD), the core component in our proposed speaker diarization system that
was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription
(M2MeT) challenge. These techniques are designed to handle multi-speaker
conversations in real-world meeting scenarios with high speaker-overlap ratios
and under heavy reverberant and noisy condition. First, for data preparation
and augmentation in training TS-VAD models, speech data containing both real
meetings and simulated indoor conversations are used. Second, in refining
results obtained after TS-VAD based decoding, we perform a series of
post-processing steps to improve the VAD results needed to reduce diarization
error rates (DERs). Tested on the ALIMEETING corpus, the newly released
Mandarin meeting dataset used in M2MeT, we demonstrate that our proposed system
can decrease the DER by up to 66.55/60.59% relatively when compared with
classical clustering based diarization on the Eval/Test set.
