Abstract: One of the most exciting areas of signal processing is speech processing; speech contains many features or characteristics that can discriminate the identity of the person. The human voice is considered one of the important biometric characteristics that can be used for person identification. This work is concerned with studying the effect of appropriate extracted features from various levels of discrete wavelet transformation (DWT) and the concatenation of two techniques (discrete wavelet and curvelet transform ) and study the effect of reducing the number of features by using principal component analysis (PCA) on speaker identification. Backpropagation (BP) neural network was also introduced as a classifier.

Feras E. Abualadas, Akram M. Zeki, Muzhir Shaban Al-Ani and Az-Eddine Messikh, “Speaker Identification based on Hybrid Feature Extraction Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100342

@article{Abualadas2019,
title = {Speaker Identification based on Hybrid Feature Extraction Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100342},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100342},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Feras E. Abualadas and Akram M. Zeki and Muzhir Shaban Al-Ani and Az-Eddine Messikh}
}