Research Article Open Access

Speaker Identification: A Hybrid Approach Using Neural Networks and Wavelet Transform

Muzhir Shaban Al-Ani, Thabit Sultan Mohammed and Karim M. Aljebory

Abstract

In speaker identification systems, a database is constructed from the speech samples of known speakers. The approach implemented in this paper is hybrid, where the wavelet transform and neural networks are used together to form a system with improved performance. Features are extracted by applying a discrete wavelet transform (DWT), while a neural network (NN) is used for formulating the system database and for handling the task of decision making. The neural network is trained using inputs, which are the feature vectors. A criteria depends on both false acceptance ratio (FAR) and false rejection ratio (FRR) is used to evaluate the system performance. For experimenting the proposed system, a set of 25 randomly aged male and female speakers was used. Results of admitting the members of this set to a secure system were computed and presented. The evaluation criteria parameters obtained are; FAR=14.5% and FRR=24.5%

Journal of Computer Science
Volume 3 No. 5, 2007, 304-309

DOI: https://doi.org/10.3844/jcssp.2007.304.309

Submitted On: 13 January 2007 Published On: 31 May 2007

How to Cite: Al-Ani, M. S., Mohammed, T. S. & Aljebory, K. M. (2007). Speaker Identification: A Hybrid Approach Using Neural Networks and Wavelet Transform . Journal of Computer Science, 3(5), 304-309. https://doi.org/10.3844/jcssp.2007.304.309

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Keywords

  • Speaker identification
  • speaker recognition
  • discrete wavelet transform
  • multi-valued neural networks