An investigation on back-end for speaker recognition in multi-session enrollment

Abstract

This study explores various back-end classifiers for robust speaker recognition in multi-session enrollment, with emphasis on optimal utilization and organization of speaker information present in the development data. Our objective is to construct a highly discriminative back-end framework by fusing several back-ends on an i-vector system framework. It is demonstrated that, by using different information/data configuration and modeling schemes, performance of the fused system can be significantly improved compared to an individual system using a single front-end and back-end. Averaged across both genders, we obtain a relative improvement in EER and minDCF by 56.5% and 49.4%, respectively. Consistent performance gains obtained using the proposed strategy validates its effectiveness. This system is part of the CRSS' NIST SRE 2012 submission system.