Hybrid networks based on RBFN and GMM for speaker recognition

In this paper, a hybrid network based on the combination of Radial Basis Function Networks (RBFNs) and Gaussian Mixture Models (GMMs) is proposed and used for speaker recognition. The hybrid network is a hierarchical one, where a GMM is built for each speaker and an RBFN is built for each group of speakers. The GMMs and RBFNs are trained independently. The RBFNs are used as a rst stage coarse classi er and the GMMs are used as the nal classi er. For each RBFN, only the rst several candidates are chosen to take part in the nal classi cation. The hybrid system is used for the SPIDRE database speaker recognition. Some experiments were carried out to choose the proper structure and parameters of RBFNs and GMMs. After using RBFNs, about 40% speakers were excluded without decreasing the performance. If the most confusable speaker sets in GMMs are grouped into RBFNs, the performance of GMMs can be increased more by using RBFNs.