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

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 recitati...

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Main Authors: Alqadasi, Ammar Mohammed Ali, Sunar, Mohd. Shahrizal, Turaev, Sherzod, Abdulghafor, Rawad, Salam, Md. Sah, Alashbi, Abdulaziz Ali Saleh, Ahmed Salem, Ali, H. Ali, Mohammed A.
Format: Article
Language:English
Published: MDPI 2023
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Online Access:http://eprints.utm.my/106606/1/MohdShahrizalSunar2023_RuleBasedEmbeddedHMMsPhonemeClassification.pdf
http://eprints.utm.my/106606/
http://dx.doi.org/10.3390/electronics12010176
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spelling my.utm.1066062024-07-14T09:23:08Z http://eprints.utm.my/106606/ Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition Alqadasi, Ammar Mohammed Ali Sunar, Mohd. Shahrizal Turaev, Sherzod Abdulghafor, Rawad Salam, Md. Sah Alashbi, Abdulaziz Ali Saleh Ahmed Salem, Ali H. Ali, Mohammed A. QA75 Electronic computers. Computer science 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. MDPI 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/106606/1/MohdShahrizalSunar2023_RuleBasedEmbeddedHMMsPhonemeClassification.pdf Alqadasi, Ammar Mohammed Ali and Sunar, Mohd. Shahrizal and Turaev, Sherzod and Abdulghafor, Rawad and Salam, Md. Sah and Alashbi, Abdulaziz Ali Saleh and Ahmed Salem, Ali and H. Ali, Mohammed A. (2023) Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition. Electronics (Switzerland), 12 (1). pp. 1-24. ISSN 2079-9292 http://dx.doi.org/10.3390/electronics12010176 DOI : 10.3390/electronics12010176
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Alqadasi, Ammar Mohammed Ali
Sunar, Mohd. Shahrizal
Turaev, Sherzod
Abdulghafor, Rawad
Salam, Md. Sah
Alashbi, Abdulaziz Ali Saleh
Ahmed Salem, Ali
H. Ali, Mohammed A.
Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
description 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.
format Article
author Alqadasi, Ammar Mohammed Ali
Sunar, Mohd. Shahrizal
Turaev, Sherzod
Abdulghafor, Rawad
Salam, Md. Sah
Alashbi, Abdulaziz Ali Saleh
Ahmed Salem, Ali
H. Ali, Mohammed A.
author_facet Alqadasi, Ammar Mohammed Ali
Sunar, Mohd. Shahrizal
Turaev, Sherzod
Abdulghafor, Rawad
Salam, Md. Sah
Alashbi, Abdulaziz Ali Saleh
Ahmed Salem, Ali
H. Ali, Mohammed A.
author_sort Alqadasi, Ammar Mohammed Ali
title Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
title_short Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
title_full Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
title_fullStr Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
title_full_unstemmed Rule-based embedded HMMs phoneme classification to improve Qur'anic recitation recognition
title_sort rule-based embedded hmms phoneme classification to improve qur'anic recitation recognition
publisher MDPI
publishDate 2023
url http://eprints.utm.my/106606/1/MohdShahrizalSunar2023_RuleBasedEmbeddedHMMsPhonemeClassification.pdf
http://eprints.utm.my/106606/
http://dx.doi.org/10.3390/electronics12010176
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score 13.211869