A robust unsupervised speaker clustering of speech utterances

This paper aims at developing and investigating efficient, robust and unsupervised algorithm for speaker clustering. Each utterance is modeled as a single Gaussian model distribution. A novel distance metric is proposed in this paper for the purpose of determining stopping criteria. The advantage of the proposed method is that it achieves comparable performance without requiring an adjusting threshold term. In this paper, we adopt the framework of agglomerative hierarchical clustering (AHC) with the merging criterion using Kullback-Leibler (KL) distance. The proposed stopping criterion can ensure a right number of speaker clusters. The efficiency of the proposed algorithm is demonstrated with various experiments on data from NIST and HUB5, respectively.