Automatic Speaker

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 signiicantly to the word error rate (WER) reduction in unsupervised adaptation. From our experiments , the algorithm seldom misclassiies segments from the same speaker into diierent 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 improves unsupervised adaptation as much as the hand labeled ideal case where the clusters are generated based on true speaker, channel and background condition.