Speaker identification using a polynomial-based classifier

A new set of techniques for using polynomial-based classifiers for speaker identification is examined. This set of techniques makes application of polynomial classifiers practical for speaker identification by enabling discriminative training for large data sets. The training technique is shown to be invariant to fixed liftering and affine transforms of the feature space. Efficient methods for new class addition, low-complexity retraining, and identification across large populations are given. The method is illustrated by application to the YOHO database.