Text-Independent Speaker Identification Using VQ-HMM Model Based Multiple Classifier System

Every feature extraction and modeling technique of voice/speech is not suitable in all type of environments. In many real life applications, it is not possible to use all type of feature extraction and modeling techniques to design a single classifier for speaker identification tasks because it will make the system complex. So instead of exploring more techniques or making the system complex it is more reasonable to develop the classifier by using existing techniques and then combine them by using different combination techniques to enhance the performance of the system. Thus, this paper describes the design and implementation of a VQ-HMM based Multiple Classifier System by using different combination techniques. The results show that the developed system by using confusion matrix significantly improve the identification rate.