This is a Kaldi recipe for The First DIHARD Speech Diarization Challenge.
DIHARD is a new annual challenge focusing on "hard" diarization; that is,
speech diarization for challenging corpora where there is an expectation that
the current state-of-the-art will fare poorly, including, but not limited
to: clinical interviews, extended child language acquisition recordings,
YouTube videos and "speech in the wild" (e.g., recordings in restaurants)
See https://coml.lscp.ens.fr/dihard/index.html for details.
The subdirectories "v1" and so on are different speaker diarization
recipes. The recipe in v1 demonstrates a standard approach using a
full-covariance GMM-UBM, i-vectors, PLDA scoring and agglomerative
hierarchical clustering. The example in v2 demonstrates DNN speaker
embeddings, PLDA scoring and agglomerative hierarchical clustering.