  The recently proposed VBx diarization method uses a Bayesian hidden Markov
model to find speaker clusters in a sequence of x-vectors. In this work we
perform an extensive comparison of performance of the VBx diarization with
other approaches in the literature and we show that VBx achieves superior
performance on three of the most popular datasets for evaluating diarization:
CALLHOME, AMI and DIHARDII datasets. Further, we present for the first time the
derivation and update formulae for the VBx model, focusing on the efficiency
and simplicity of this model as compared to the previous and more complex BHMM
model working on frame-by-frame standard Cepstral features. Together with this
publication, we release the recipe for training the x-vector extractors used in
our experiments on both wide and narrowband data, and the VBx recipes that
attain state-of-the-art performance on all three datasets. Besides, we point
out the lack of a standardized evaluation protocol for AMI dataset and we
propose a new protocol for both Beamformed and Mix-Headset audios based on the
official AMI partitions and transcriptions.
