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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 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.