Abstract—Holy Quran Reader Identification is the process of identifying the reader or reciter of the Holy Quran based on several features in the corresponding acoustic wave. In this research, we build our own corpus, which contains 15 known readers of the Holy Quran. The Mel-Frequency Cepstrum Coefficients (MFCC) are used for the extraction of these features from the input acoustic signal. These MFCCs are the reader’s features matrix, which is used for recognition via Support Vector Machine (SVM) and Artificial Neural Networks (ANN). According to our experimental results, the Holy Quran Reader Identification System identifies the reader with 96.59% accuracy when using SVM, in contrast to accuracy of 86.1% when using ANN.
Index Terms—Reader identification, feature extraction, Mel-Frequency Cepstrum Coefficients (MFCC), support vector machine (SVM), Artificial Neural Networks (ANN).
Khalid M. O. Nahar, Moyawiah Al-Shannaq, Ahmad Manasrah, Rafat Alshorman are with Department of Computer Sciences, Faculty of IT and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan (Corresponding author: Khalid M. O. Nahar; e-mail: firstname.lastname@example.org).
Iyad Alazzam is with Department of Computer Information System, Faculty of IT and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.
Cite: Khalid M. O. Nahar, Moyawiah Al-Shannaq, Ahmad Manasrah, Rafat Alshorman, and Iyad Alazzam, "A Holy Quran Reader/Reciter Identification System Using Support Vector Machine," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 458-464, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).