This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge. For tracks 1 and 2 the systems were based on performing agglomerative hierarchical clustering (AHC) over x-vectors, followed by the Bayesian Hidden Markov Model (HMM) with eigenvoice priors applied at x-vector level followed by the same approach applied at frame level. For tracks 3 and 4, the systems were based on performing AHC using x-vectors extracted on all channels.
Cite
@article{arxiv.1910.08847,
title = {BUT System Description for DIHARD Speech Diarization Challenge 2019},
author = {Federico Landini and Shuai Wang and Mireia Diez and Lukáš Burget and Pavel Matějka and Kateřina Žmolíková and Ladislav Mošner and Oldřich Plchot and Ondřej Novotný and Hossein Zeinali and Johan Rohdin},
journal= {arXiv preprint arXiv:1910.08847},
year = {2019}
}