Journal of Engineering (Mar 2006)

A WAVELET NEURAL NETWORK RAMWORK FOR SPEAKER IDNTIFCATION

  • W. A. Mahmoud,
  • Dhiadeen.M. Salih,
  • Saleem M-R. Taha

DOI
https://doi.org/10.31026/j.eng.2006.01.17
Journal volume & issue
Vol. 12, no. 01

Abstract

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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 the proposed scheme