  In this work, a novel solution to the speaker identification problem is
proposed through minimization of statistical divergences between the
probability distribution (g). of feature vectors from the test utterance and
the probability distributions of the feature vector corresponding to the
speaker classes. This approach is made more robust to the presence of outliers,
through the use of suitably modified versions of the standard divergence
measures. The relevant solutions to the minimum distance methods are referred
to as the minimum rescaled modified distance estimators (MRMDEs). Three
measures were considered - the likelihood disparity, the Hellinger distance and
Pearson's chi-square distance. The proposed approach is motivated by the
observation that, in the case of the likelihood disparity, when the empirical
distribution function is used to estimate g, it becomes equivalent to maximum
likelihood classification with Gaussian Mixture Models (GMMs) for speaker
classes, a highly effective approach used, for example, by Reynolds [22] based
on Mel Frequency Cepstral Coefficients (MFCCs) as features. Significant
improvement in classification accuracy is observed under this approach on the
benchmark speech corpus NTIMIT and a new bilingual speech corpus NISIS, with
MFCC features, both in isolation and in combination with delta MFCC features.
Moreover, the ubiquitous principal component transformation, by itself and in
conjunction with the principle of classifier combination, is found to further
enhance the performance.
