  This paper describes the system developed by the BUT team for the fourth
track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on
the VoxConverse dataset. The system consists of signal pre-processing, voice
activity detection, speaker embedding extraction, an initial agglomerative
hierarchical clustering followed by diarization using a Bayesian hidden Markov
model, a reclustering step based on per-speaker global embeddings and
overlapped speech detection and handling. We provide comparisons for each of
the steps and share the implementation of the most relevant modules of our
system. Our system scored second in the challenge in terms of the primary
metric (diarization error rate) and first according to the secondary metric
(Jaccard error rate).
