Speaker verification score normalization using speaker model clusters

Among the various proposed score normalizations, T- and Z-norm are most widely used in speaker verification systems. The main idea in these normalizations is to reduce the variations in impostor scores in order to improve accuracy. These normalizations require selection of a set of cohort models or utterances in order to estimate the impostor score distribution. In this paper we investigate basing this selection on recently-proposed speaker model clusters (SMCs). We evaluate this approach using the NTIMIT and NIST-2002 corpora and compare against T- and Z-norm which use other cohort selection methods. We also propose three new normalization techniques, Δ-, ΔT- and TC-norm, which also use SMCs to estimate the normalization parameters. Our results show that we can lower the equal error rate and minimum decision cost function with fewer cohort models using SMC-based score normalization approaches.