Speaker clustering using direct maximization of a BIC-based score

This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the required number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning of utterances based on how high the level of within-cluster homogeneity can be achieved at the expense of increasing the number of clusters. Unlike the existing methods, in which BIC is used only to determine the optimal number of clusters, the proposed method uses BIC in conjunction with a genetic algorithm to determine the optimal cluster where each utterance should be located. The experimental results show that the proposed speakerclustering method outperforms the conventional methods.