Speaker recognition is the process of automatically recognizing the speaker by analyzing individual information contained in the speech waves. We discuss the development of an intelligent system for text-dependent speaker recognition. The system comprises two main modules, a wavelet-based signal-processing module for feature extraction of speech waves, and an artificial neural network-based classifier module to identify and categorize the speakers. The wavelet is used in de-noising and in compressing the speech signals. The wavelet family that we used is the Daubechies (1988) wavelets. After extracting the necessary features from the speech waves, the features were then fed to a neural-network-based classifier to identify the speakers. We have implemented the fuzzy ARTMAP (FAM) network in the classifier module to categorize the de-noised and compressed signals. The proposed intelligent learning system has been applied to a case study of text-dependent speaker recognition problem.
