Performance analysis of text-dependent speaker recognition system based on template model based classifiers

Self-Defined Text-Dependent Wake-Up-Words Speaker Recognition System

“…Then they used MFCC feature matrix to train a GMM with 50 components by Expectation-Maximization Algorithm. The Gaussians components were first shown to represent characteristic spectral shapes from the phonetic sounds which comprise a person's voice [15]- [16]. Identification performance of the GMM is insensitive to the method of model initialization.…”