Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

AL-JARRAH, Mohammad A, AL-JARRAH, Ahmad, JARRAH, Amin, ALSHURBAJI, Mohammad, MAGABLEH, Sharaf K, AL-TAMIMI, Abdel-Karim, BZOOR, Nisreen and AL-SHAMALI, Mamoun O (2022). Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm. Revue d'Intelligence Artificielle, 36 (6), 815-823.

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Abstract

The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method.

Item Type:	Article
Additional Information:	25/01/2023 On publisher's website - "Creative Commons CC BY 4.0 license applies to any article published in IIETA journals" https://iieta.org/Journals/RIA/Publication%20Ethics%20and%20Malpractice%20Statement
Uncontrolled Keywords:	Artificial Intelligence & Image Processing
Identification Number:	https://doi.org/10.18280/ria.360601
Page Range:	815-823
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	25 Jan 2023 16:53
Last Modified:	11 Oct 2023 17:48
URI:	https://shura.shu.ac.uk/id/eprint/31335

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