Segmental K-Means Initialization for SOM-Based Speaker Clustering

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.