Development of a speaker recognition system using wavelets and artificial neural networks

We address the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural network-based classifier. We have conducted a number of experiments to investigate the applicability of a discrete Wavelet transform (DWT) in extracting discriminative features from the speech signals, and have examined various models from the adaptive resonance theory (ART) family of neural networks in classifying the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classifiers have yielded very promising recognition accuracy at more than 81%.