Joint Cohort Normalization in a Multi-Feature Speaker Verification System

In this paper we propose a new fusion technique, termed Joint Cohort Normalization Fusion, where the information fusion is done prior to the likelihood ratio test in a speaker verification system. The performance of the technique is compared against two popular types of fusion: feature vector concatenation and expert opinion fusion, for fusion of Mel Frequency Cepstral Coe#cients (MFCC), MFCC with Cepstral Mean Subtraction (CMS) and Maximum Auto-Correlation Values (MACV) features. In experiments on the NTIMIT database, the proposed technique is shown, in most cases, to outperform the popular methods