Variable parameter speaker verification system based on hidden Markov modeling

Abstract

A text-independent speaker verification system based on an adaptive vocal tract model which emulates the vocal tract of the speaker is described. Each speaker is represented by a set of feature vectors derived from speech segments belonging to different classes of phonemes. Linear predictive hidden Markov modeling and maximum-likelihood Viterbi decoding are applied to a speech utterance to obtain different classes of phonemes pronounced by a speaker. It is shown that different classes of phonemes are not equally effective in discriminating between speakers and that verification performance can be considerably improved by separately classifying speech segments representing each broad phonetic category as belonging to an impostor or as belonging to the true speaker. A weighted linear combination of scores for individual categories can be used as the final verification score. The weights are chosen to reflect the effectiveness of particular classes of phonemes in discriminating between speakers and are adjusted to maximize the verification performance. >