  This paper describes the submission to the speaker diarization track of
VoxCeleb Speaker Recognition Challenge 2021 done by North America Bixby Lab of
Samsung Research America. Our speaker diarization system consists of four main
components such as overlap speech detection and speech separation, robust
speaker embedding extraction, spectral clustering with fused affinity matrix,
and leakage filtering-based postprocessing. We evaluated our system on the
VoxConverse dataset and the challenge evaluation set, which contain natural
conversations of multiple talkers collected from YouTube. Our system obtained
4.46%, 6.39%, and 6.16% of the diarization error rate on the VoxConverse
development, test, and the challenge evaluation set, respectively.
