SOM as likelihood estimator for speaker clustering

Abstract

A new approach is presented for clustering the speakers from unlabeled and unsegmented conversation, when the number of speakers is unknown. In this approach, Self-Organizing-Map (SOM) is used as likelihood estimators for speaker model. For estimation of the number of clusters the Bayesian Information Criterion (BIC) is applied. This approach was tested on the NIST 1996 HUB-4 evaluation test in terms of speaker and cluster purities. Results indicate that the combined SOM-BIC approach can lead to better clustering results than the baseline system. 1.