On Performance Improvement of a Speaker Verification System Using Vector Quantization, Cohorts and Hybrid Cohort-World Models

Abstract

This paper presents the use of distance normalization techniques in order to improve speaker verification system performance. These techniques provide a dynamic threshold that compensates for the trial-to-trial variations and replaces the fixed threshold used in the classical speaker verification approach. Two methods are described: the cohort model normalization and a new and original hybrid cohort-world model normalization. These methods are compared from the point of view of storage space requirements and computational effort. Two algorithms are proposed: one uses existing user models, and the other creates new models. The algorithms were evaluated using the YOHO database and a proprietary database. The results showed that using these methods, the errors of false rejection are significantly reduced for a constant false acceptance error, when the cohort size is increasing. The algorithms also involve fewer computational resources than other algorithms, making them more suitable for commercial application.