Kernel-Based Speaker Clustering for Rapid Speaker Adaptation

Abstract

Speaker clustering is a widely used technique in speaker adaptation, especially since it can be easily combined with adaptation methods such as MAP or MLLR. In this paper we present and evaluate a new speaker adaptation method using a kernel-based speaker clustering algorithm inspired by the classical K-means and based on one-class support vector machines. We find that this adaptation method outperforms other conventional clustering techniques such as K-means and gender clustering with only small amounts of adaptation data (i.e. less than 10 sec).