A Wavelet Neural Network Ramwork for Speaker Idntifcation


This paper introduces a new model-free identification methodology to detect and identify speakers and recognize them. The basic module of the methodology is a novel multi-dimensional wavelet neural network . The WNN approach include: a universal approximator ; the time – frequency localization : property of wavelets leads to reduced networks at a given level of performance ; The construct used as the feature mode classifier . Wavelet transform has been successfully applied to the processing of non – stationary speech signal and the feature vector that obtained becomes the input to the wavelet neural network which is trained off-line to map features to used for the classification procedure. An example is employed to illustrate the robustness and effectiveness of proposed scheme.