  This paper describes the speaker diarization systems developed for the Second
DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides
describing the system, which considerably outperformed the challenge baselines,
we also focus on the lessons learned from numerous approaches that we tried for
single and multi-channel systems. We present several components of our
diarization system, including categorization of domains, speech enhancement,
speech activity detection, speaker embeddings, clustering methods,
resegmentation, and system fusion. We analyze and discuss the effect of each
such component on the overall diarization performance within the realistic
settings of the challenge.
