Multistage Decision Schemes for Speaker Recognition

Speaker recognition schemes which work satisfactorily for small populations often fail when the number of classes is very large. One way of solving such problems is to go in for multistage classification schemes. The basic technique is to successively reduce the number of classes in several stages using one feature at each stage and when the number of classes is less than a predetermined value then the final decision is made. The whole scheme is designed so that the probability of error is fixed at an acceptable level. The computational cost of such a multistage scheme depends on the features used at each stage and the cost of measurement of each feature. The features to be used at each stage are determined so as to reduce the average computational cost for making a decision. This procedure is formulated as a stochastic optimal control problem and is illustrated by designing speaker recognition system for 60 speakers. The overall accuracy of the system is 97.2 %