@article {10.3844/jcssp.2007.304.309,
article_type = {journal},
title = {Speaker Identification: A Hybrid Approach Using Neural Networks and Wavelet Transform },
author = {Al-Ani, Muzhir Shaban and Mohammed, Thabit Sultan and Aljebory, Karim M.},
volume = {3},
number = {5},
year = {2007},
month = {May},
pages = {304-309},
doi = {10.3844/jcssp.2007.304.309},
url = {https://thescipub.com/abstract/jcssp.2007.304.309},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}