On text independent speaker identification using a quadratic classifier with optimal features

In most speaker identification or verification systems it is assumed that all speakers have the same feature covariance matrices. This assumption simplifies the classification algorithm since it yields a linear classifier. Speakers, however, differ not only in their mean feature vector, but in their covariance matrix as well. The use of a speaker's individual covariance matrix results in a quadratic classifier. If a common covariance matrix is assumed, a common optimal feature space can be determined. With an individual covariance matrix, each speaker is optimally recognized in an individual feature space. The recognition scheme therefore requires the matching of an unknown speaker with the templates defined over different feature spaces. The use of the quadratic classifier together with the individual feature space is shown to drastically improve recognition accuracy while the added memory requirements are shown to be negligible. The suggested quadratic classifier, with individual optimal feature vectors, has been tested using a speaker identification system with six male speakers. In terms of a given separation measure, the quadratic classifier yields improvements of about 2 times over the conventional method.