Rule-based embedded HMMs phoneme classification to improve Qur’anic recitation recognition

Mohammed Ali Alqadasi, Ammar and Sunar, Mohd Shahrizal and Turaev, Sherzod and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Salam, Md Sah and Alashbi, Abdulaziz Ali Saleh and Ahmed Salem, Ali and Ali, Mohammed A. H. (2022) Rule-based embedded HMMs phoneme classification to improve Qur’anic recitation recognition. Electronics, 12 (1). pp. 1-24. ISSN 2079-9292

	PDF (Journal) - Published Version Restricted to Repository staff only Download (5MB) | Request a copy
Preview	PDF (SCOPUS) - Supplemental Material Download (207kB) | Preview

Abstract

Phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. A mispronunciation of Arabic short vowels or long vowels can change the meaning of a complete sentence. However, correctly distinguishing phonemes with vowels in Quranic recitation (the Holy book of Muslims) is still a challenging problem even for state-of-the-art classification methods, where the duration of the phonemes is considered one of the important features in Quranic recitation, which is called Medd, which means that the phoneme lengthening is governed by strict rules. These features of recitation call for an additional classification of phonemes in Qur’anic recitation due to that the phonemes classification based on Arabic language characteristics is insufficient to recognize Tajweed rules, including the rules of Medd. This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. The phonemes of the Qur’anic dataset contain 21 Ayats collected from 30 reciters and are carefully analyzed from a baseline HMM-based speech recognition model. Using the Hidden Markov Model with tied-state triphones, a set of phoneme classification models optimized based on duration is constructed and integrated into a Quranic phoneme classification method. The proposed algorithm achieved outstanding accuracy, ranging from 99.87% to 100% according to the Medd type. The obtained results of the proposed algorithm will contribute significantly to Qur’anic recitation recognition models.

Item Type:	Article (Journal)
Uncontrolled Keywords:	phoneme classification; phoneme duration; pattern recognition; Arabic vowels classification; recitation recognition; Tajweed recognition; speech recognition
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Rawad Abdulghafor
Date Deposited:	03 Jan 2023 09:56
Last Modified:	20 Feb 2023 15:36
URI:	http://irep.iium.edu.my/id/eprint/102459

Actions (login required)

View Item