Speaker Verification System based on the Cerebellum Architecture

The Cerebellar Model Articulation Controller (CMAC) and fuzzy systems have been active areas of research since its initial introduction. Fuzzy CMAC is basically a CMAC that is coupled with a fuzzy system. This paper presents the incorporation of fuzzy systems into CMAC with Approximate Analogical Reasoning Scheme (AARS) as its inference rules and Discrete Incremental Clustering (DIC) as its technique to classify the input space, which is used for the speaker verification system. Features extracted from a known speaker using either Mel Frequency Cepstrum Coefficients (MFCC) or Delta Mel Frequency Cepstrum Coefficients (DMFCC) methods are used as the inputs for the system. The variables used for the performance analysis are False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER), training and testing time.