Speaker verification using speaker- and test-dependent fast score normalization

Abstract

A novel score normalization scheme for speaker verification is presented. The proposed technique is based on the widely used test-normalization method (Tnorm), which compensates test-dependent variability using a fixed cohort of impostors. The new procedure selects a speaker-dependent subset of impostor models from the fixed cohort using a distance-based criterion. Selection of the sub-cohort is made using a distance measure based on a fast approximation of the Kullback–Leibler (KL) divergence for Gaussian mixture models (GMM). The proposed technique has been called KL-Tnorm, and outperforms Tnorm in computational efficiency. Experimental results using NIST 2005 Speaker Recognition Evaluation protocol also show a stable performance improvement of our method on standard speaker recognition systems.