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 coefficients (MFCC), MFCC with cepstral mean subtraction and maximum auto-correlation values features. In experiments on the NTIMIT database, the proposed technique is shown, in most cases, to outperform the popular methods