A 2-LEVEL CLASSIFIER FOR TEXT-INDEPENDENT SPEAKER IDENTIFICATION

A two-level scheme for speaker identification is proposed. The first c lassifier level is based on the self-organizing map (SOM) of Kohonen. LPCC coefficients are used as input vectors for this classifier. LPCC coefficients are passed again through the already trained SOMs and as result the prototype distribution maps (PDMs) are obtained. The PDMs a re the input for the second classifier level. The second level consist s of multilayer perceptron (MLP) networks for each speaker. The first level of the classifier is a preprocessing procedure for the second le vel, where the final classification is made. The goal of the proposed approach is to combine the advantages of the two type of networks into one classification scheme in order to achieve higher identification a ccuracy. The experiments show an increased accuracy of the proposed tw o-level classifier, especially in the case of noise-corrupted signals.