Text-independent speaker verification using priority ordered radial basis function networks
优先度排序RBF神经网络在与文本无关说话人确认中的应用

The structure and algorithm of Priority Ordered Radial Basis Function (PORBF) Networks is introduced. The concrete training algorithm, calculational methods of likelihood score and verification rule, used for text-independent speaker verification, are proposed. To enhance the generalization ability, the compressing vectors are applied to construct the inhibitory samples set and three methods including sequential selection, nearest neighbor selection and furthest distance selection are presented for the choose of anti-speakers. Moreover, the outputs of neurons are weighted by a descendent array. Using these algorithms and methods, the performance is examined by a series of experiments. The results show that under the identical experiment conditions, when the inhibitory set is composed of anti-speakers' compressing vectors selected using nearest neighbor method, the Equal Error Rate (EER) using PORBF networks can decreased to 6.83% from 10.56% using conventional VQ method. For speaker verification, the PORBF network provides better performance than the VQ classifier.