Speaker Identification: A Hybrid Approach Using Neural Networks and Wavelet Transform
Muzhir Shaban Al-Ani, Thabit Sultan Mohammed and Karim M. Aljebory
DOI : 10.3844/jcssp.2007.304.309
Journal of Computer Science
Volume 3, Issue 5
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%
© 2007 Muzhir Shaban Al-Ani, Thabit Sultan Mohammed and Karim M. Aljebory. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.