A novel method for selecting the number of clusters in a speaker diarization system

This paper introduces the cluster score (C-score) as a measure for determining a suitable number of clusters when performing speaker clustering in a speaker diarization system. C-score finds a trade-off between intra-cluster and extra-cluster similarities, selecting a number of clusters with cluster elements that are similar between them but different to the elements in other clusters. Speech utterances are represented by Gaussian mixture model mean supervectors, and also the projection of the supervectors into a low-dimensional discriminative subspace by linear discriminant analysis is assessed. This technique shows robustness to segmentation errors and, compared with the widely used Bayesian information criterion (BIC)-based stopping criterion, results in a lower speaker clustering error and dramatically reduces computation time. Experiments were run using the broadcast news database used for the Albayzin 2010 Speaker Diarization Evaluation.