Normalized discriminant analysis with application to a hybrid speaker-verification system

A modified linear discriminant analysis technique for speaker verification, referred to here as normalized discriminant analysis (NDA), is presented. Using this technique it is possible to design an efficient linear classifier with very limited training data and to generate normalized discriminant scores with comparable magnitudes for different classifiers. The NDA technique is applied to a classifier for speaker verification based on speaker specific information obtained when utterances are processed with speaker independent models. In experiments conducted on a network based telephone database, the NDA technique provides an equal-error rate of 6.13% while the classifier using Fisher linear discriminant analysis has an equal-error rate of 18.18%. Furthermore, when the NDA combined with HMM approach in a hybrid speaker verification system, the rate was reduced from 5.30% (HMM with cohort normalization) to 4.32%.