  This paper introduces the third DIHARD challenge, the third in a series of
speaker diarization challenges intended to improve the robustness of
diarization systems to variation in recording equipment, noise conditions, and
conversational domain. The challenge comprises two tracks evaluating
diarization performance when starting from a reference speech segmentation
(track 1) and diarization from raw audio scratch (track 2). We describe the
task, metrics, datasets, and evaluation protocol.
