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Speaker verification is performed by comparing the output probabilities of two Markov models of the same phonetic unit. One of these Markov models is speaker-specific, being built from utterances from the speaker whose identity is to be verified. The second model is built from utterances from a large population of speakers. The performance of the system is improved by treating the pair of models as a connectionist network, an alpha-net, which then allows discriminative training to be carried out. Experimental results show that adapting the spectral observation probabilities of each state of the model by the back propagation of errors can correct misclassification errors. The real-time implementation of the system produced an average digit error rate of 4.5% and only one misclassification in 600 trials using a five-digit sequence.>
Carey et al. (Tue,) studied this question.