Segmental K-Means initialization for SOM-based speaker clustering

Abstract

A new approach for initial assignment of data in a speaker clustering application is presented. This approach employs segmental k-means clustering algorithm prior to competitive based learning. The clustering system relies on self-organizing maps (SOM) for speaker modeling and as a likelihood estimator. Performance is evaluated on 108 two speaker conversations taken from LDC CALLHOME American English Speech corpus using NIST criterion and shows an improvement of 20%-30% in cluster error rate (CER) relative to the randomly initialized clustering system. The number of iterations was reduced significantly, which contributes to both speed and efficiency of the clustering system.