Abstract

This paper presents a fully automatic speaker clustering algorithm, which consists of three components: building a distance matrix based on Gaussian models of the acoustic segments; performing hierarchical clustering on the distance matrix with the prior assumption that consecutive segments should be more likely to come from the same speaker; and selecting the best clustering solution automatically by minimizing the within-cluster dispersion with some penalty against too many clusters. We applied this automatic speaker clustering technique in 1996 Hub4 evaluation, and the results show that it contributed significantly to the word error rate (WER) reduction in unsupervised adaptation. From our experiments, the algorithm seldom misclassifies segments from the same speaker into different clusters. We used the same clustering procedure for both partitioned evaluation (PE) and unpartitioned evaluation (UE) tests [1]. Experiments also show that this automatic speaker clustering algorithm imp...

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