A new approach for initial assignment of data in a speaker clustering application is presented. This approach employs Weighted Segmental K-Means clustering algorithm prior to competitive based learning. The clustering system relies on Self-Organizing Maps (SOM) for speaker modeling and likelihood estimation. Performance is evaluated on 108 two speaker conversations taken from LDC CALLHOME American English Speech corpus using NIST criterion and shows an improvement of approximately 48% 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.
Bibliographic reference. Ben-Harush, Oshry / Lapidot, Itshak / Guterman, Hugo (2008): "Weighted segmental k-means initialization for SOM-based speaker clustering", In INTERSPEECH-2008, 24-27.