README.txt 863 Bytes
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.