The Modeling of the Quranic Alphabets' Correct Pronunciation for Adults and Children Experts

Ahmad, Salmiah; Badruddin, Syarifah N. S.; Hashim, Nik Nur Wahidah Nik; Embong, Abdul Halim; Altalmas, Tareq; Hasan, Surul S.

doi:10.1109/cais.2019.8769590

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The sound spectrum can be represented by formant frequencies which show the greater intensity of sound quality. The quality of sound is greatly shown using formant frequencies, especially the characteristic of sound of the consonant [6]. In this case, the formant frequencies of f 1 , f 2 , f 3 and f 4 as illustrated in Fig.…”

Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

“…In this case, the formant frequencies of f 1 , f 2 , f 3 and f 4 as illustrated in Fig. 1 are used as features to model the Quranic alphabet pronunciation [6]. Theoretically, the combination of formant frequencies of f 1 , f 2 , f 3 and f 4 from speech production should be able to describe the characteristics of the letters during pronunciation for each of the 28 hijaiyah letters (Arabic letters).Furthermore, the changes of formant frequencies of f 1 , f 2 , f 3 and f 4 can be used to represent the characteristic of vowel, consonants and its combination [7][8].…”

Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

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Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Adam¹,

Shafie²,

Abas³

et al. 2021

IJACSA

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The use of technological speech recognition systems with a variety of approaches and techniques has grown rapidly in a variety of human-machine interaction applications. Further to this, a computerized assessment system to identify errors in reading the Qur'an can be developed to practice the advantages of technology that exist today. Based on Quranic syllable utterances, which contain Tajweed rules that generally consist of Makhraj (articulation process), Sifaat (letter features or pronunciation) and Harakat (pronunciation extension), this paper attempts to present the technological capabilities in realizing Quranic recitation assessment. The transformation of the digital signal of the Quranic voice with the identification of reading errors (based on the Law of Tajweed) is the main focus of this paper. This involves many stages in the process related to the representation of Quranic syllable-based Recitation Speech Signal (QRSS), feature extraction, non-phonetic transcription Quranic Recitation Acoustic Model (QRAM), and threshold classification processes. MFCC-Formants are used in a miniature state that are hybridized with three bands in representing QRSS combined vowels and consonants. A human-guided threshold classification approach is used to assess recitation based on Quranic syllables and threshold classification performance for the low, medium, and high band groups with performances of 87.27%, 86.86%and 86.33%, respectively.

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Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

Section: Momentous Fragmentary Mfcc-formantsmentioning

confidence: 99%

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Adam¹,

Shafie²,

Abas³

et al. 2021

IJACSA

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show abstract

Sequential classification for articulation and Co-articulation classes of Al-Quran syllables pronunciations based on GMM-MLLR

Shafie¹,

Adam²,

Abas³

et al. 2021

International Conference on Food Science and Biotechnology (Fsab 2021)

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Rule-Based Embedded HMMs Phoneme Classification to Improve Qur’anic Recitation Recognition

et al. 2022

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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.

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The Modeling of the Quranic Alphabets' Correct Pronunciation for Adults and Children Experts

Cited by 3 publications

References 11 publications

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

Sequential classification for articulation and Co-articulation classes of Al-Quran syllables pronunciations based on GMM-MLLR

Rule-Based Embedded HMMs Phoneme Classification to Improve Qur’anic Recitation Recognition

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