Biometric Speaker Recognition Using Neural Networks and Wavelet Transform

The use of biometric information has been known widely for both person identification and security application. It is common knowledge that each person can be identified by the unique characteristics of one or more of person biometrics. One of the biometric characteristics of that a person can be identified by his voice. This research attempts to find the best discrete coefficient that can be selected from one or more level using neural network as classifier. This research is concerned with studying the feature extracted from discrete wavelet transformation using feed-forward Back-Propagation neural network classifiers. The neural networks trained with features those extracted from discrete wavelet transformed voice record. The reliability of the neural network architecture is studied in this research to get the best recognition rate. The data set of testing consist of different sounds recorded from ten different persons (three female and seven males) using normal PC sound recorder.