Recitation of the Holy book of Muslims, The Holy Quran, is a regions duty and hence is done with utmost care such that no mistakes are made while reading it. These mistakes may include the wrong utterance of words, misreading words, and punctuation and pronunciation mistakes. Believers of Islam are spread all over the world and hence there also can be a difference in accent. To avoid this, tajweed rules are implemented to ensure that the utterance is done according to some rules. These rules ensure that there is no variance in the recitation of the Holy book for different reciters. For further improvements, the people are encouraged to memorize the whole book and the person who does that is called a Hafiz. Having known the whole book by heart down to every word with Tajweed rules he/she can a guide to correct other learners who intend to learn by listening to learners and correcting their recitation. But the availability of a Hafiz can be a problem where Islam is not a dominant religion. Furthermore the competency and level of expertise are of epic importance. To get around this problem we have designed and developed a system E-Hafiz. It is based on an idea that Tajweed rules are used to train learners how to recite Quran. To achieve this we used on Mel-Frequency Cepstral Coefficient (MFCC) technique. We extract the features of recorded voices using MFCC and compared with experts’ voices stored in database. Any mismatch on word level is pointed out and ask the user to correct it. [Aslam Muhammad, Zia ul Qayyum, Waqar Mirza M. Saad Tanveer, Martinez-Enriquez A.M., Afraz Z. Syed: EHafiz: Intelligent System to Help Muslims in Recitation and Memorization of Quran. Life Science Journal. 2012;9(1):534-541] (ISSN:1097-8135). http://www.lifesciencesite.com. 80

E-hafiz: Intelligent system to help muslims in recitation and memorization of Quran

Purpose – The purpose of this paper is to provide a structural overview of speech recognition system for developing Quranic verse recitation recognition with tajweed checking rules function. This function has been introduced, due to support the existing and manual method of talaqqi and musyafahah method in Quranic learning process, which described as face-to-face learning process between students and teachers. Here, the process of listening, correction and repetition of the correct Al-Quran recitation took place in real-time condition. However, this method is believed to become less effective and unattractive to be implemented, especially towards the young Muslim generation who are more attracted to the latest technology. Design/methodology/approach – This paper focuses on the development of software prototype, mainly for developing an automated Tajweed checking rules engine, purposely for Quranic learning. It has been implemented and tested towards the j-QAF students at primary school in Malaysia. Findin...

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Automated tajweed checking rules engine for Quranic learning

In Islamic religion, mistakes in recitation of holy Quran (the sacred book of Muslims) are forbidden. Mistakes can be missing words, verse, misreading Harakat (pronunciations, punctuations, and accents). Thus, a hafiz/reciter who memorizes the holy Quran, needs other hafiz/tutor who listens the recitation and points oral mistakes. Due to the seriously commitment, the availability and expertise of a hafiz are also questionable. A listener can also make mistakes while hearing imputable to environmental interruptions like noise, attention. In order to tackle this issue, we designed, developed, and tested the E-hafiz system. E-hafiz is based on Mel-Frequency Cepstral Coefficient (MFCC) technique to extract voice features from Quranic verse recitation and maps them with the data collected during the training phase. Any mismatch mistake is pointed out. Testing results of short verses of Quran using the E-hafiz system are very encouraging.

Voice Content Matching System for Quran Readers

Preface. Acronyms. Notation. Introduction. Signal Processing Techniques. Stochastic Processes and Models. Linear Prediction. Scalar Quantization. Pulse Code Modulation and Its Variants. Vector Quantization. Scalar Quantization of Linear Prediction Coefficient. Linear Prediction Coding. Regular-Pulse Excitation Coders. Code-Excited Linear Prediction. The Federal Standard Version of CELP. Vector Sum Excited Linear Prediction. Low-Delay CELP. Vector Quantization of Linear Prediction Coefficient. Algebraic CELP. Mixed Excitation Linear Prediction. Source-Controlled Variable Bit-Rate CELP. Speech Quality Assessment. Appendix A: Minimum-Phase Property of the Forward Prediction-Error Filter. Appendix B: Some Properties of Line Spectral Frequency. Appendix C: Research Directions in Speech Coding. Appendix D: Linear Combiner for Pattern Classification. Appendix E: CELP: Optimal Long-Term Predictor to Minimize the Weighted Difference. Appendix F: Review of Linear Algebra: Orthogonality, Basis, Linear Independence, and the Gram-Schmidt Algorithm. Bibliography. Index.

Speech coding algorithms : foundation and evolution of standardized coders

https://cyberleninka.org/article/n/168089.pdf

Online integrity and authentication checking for Quran electronic versions

†Summary Each person’s voice is different. Thus, the Quran sound, which had been recited by most of recitors will probably tend to differ a lot from one person to another. Although those Quranic sentence were particularly taken from the same verse, but the way of the sentence in Al-Quran been recited or delivered may be different. It may produce the difference sounds for the different recitors. Those same combinations of letters may be pronounced differently due to the use of harakates. This paper seeks to provide a comprehensive review of Quran Arabic verse recitation recognition focusing on the techniques used, the advantages, and drawbacks. Areas with potential of further expansion are identified for future research for support in j-QAF learning.

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Kuala Lumpur

Papers

Quranic Verse Recitation Recognition Module for Support in j-QAF Learning: A Review