Mispronunciation Detection in Articulation Points of Arabic Letters using Machine Learning

Farooq, Javeria; Imran, Muhammad

doi:10.1109/icecube53880.2021.9628251

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The system utilizes Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) and Hidden Markov Model for accurate training and recognition. Promising results suggest an improved understanding of Tajweed rules and high recognition rates, supporting the successful implementation of the research [35]. Another study presents a sound-matching application for children to learn Quran verses independently using speech recognition scheme as shown in Figure 3.…”

Section: Independentmentioning

confidence: 63%

Quran Mobile Application: A Structured Review

Nurul Husna Mat Isa,

Noor Hazirah Abd Aziz,

Mariyah Ishak

et al. 2023

ARASET

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Mobile applications are growing rapidly and can be employed for various purposes. Presently, they have been applied widely in education. The pace of technological advancement is incredibly fast, impacting all segments of society. One noteworthy area is the facilitation of learning to read and write the Quran, where recent technological developments have significantly simplified the process. However, these applications have certain limitations as they solely concentrate on utilizing technology without incorporating more inventive approaches to aid Quran readers in interacting with, memorizing, comprehending, and establishing connections with the topics covered in the Quran. This paper aims to systematically review and analyze current research on the Quran mobile application. The study reviewed relevant literature indexed in Scopus, Web of Science (WOS), and Directory of Open Access Journals (DOAJ) databases. Consequently, three key study themes have been identified after thoroughly analyzing the “Quran mobile application”: learning and memorization, text analysis and information retrieval, and user experience and engagement. As Quran mobile applications have increased accessibility to learning the Qur’an, they frequently concentrate only on the use of technology meanwhile innovative approaches to improve engagement, memorization, comprehension, and topic connections for Quran readers remain lacking. The outcomes of this study can serve as inspiration for other researchers, providing a solid groundwork for the development and improvements in understanding the Quran.

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Section: Independentmentioning

confidence: 63%

Quran Mobile Application: A Structured Review

Nurul Husna Mat Isa,

Noor Hazirah Abd Aziz,

Mariyah Ishak

et al. 2023

ARASET

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“…Tis is just a classifcation system for recitation. Farooq and Imran [10] developed a real-time application for mispronunciation detection of Arabic letters according to Tajweed rules. Te decision of correct or incorrect pronunciation is based on articulation points of letters.…”

Section: Related Work a Lot Of Work Has Been Done To Classifymentioning

confidence: 99%

“…Accuracy Precision F1-score Al-Ayyoub et al [9] 96.4% 96.6% 96.3% Farooq and Imran [10] 98% (only words) --Ghori et al [11] 97.5% --Proposed system 97.7% 97.6% 97.4%…”

Section: Threats To Validitymentioning

confidence: 99%

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

Nigar,

Wajid,

Ajagbe

et al. 2023

Applied Computational Intelligence and Soft Computing

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The COVID-19 pandemic influenced the whole world and changed social life globally. Social distancing is an effective strategy adopted by all countries to prevent humans from being infected. Al-Quran is the holy book of Muslims and its listening and reading is one of the obligatory activities. Close contact is essential in traditional learning system; however, most of the Al-Quran learning schools were locked down to minimize the spread of COVID-19 infection. To address this limitation, in this paper, we propose a novel system using deep learning to identify the correct recitation of individual alphabets, words from a recited verse and a complete verse of Al-Quran to assist the reciter. Moreover, in the proposed approach, if the user recites correctly, his/her voice is also added to the existing dataset to leverage proposed approach effectiveness. We employ mel-frequency cepstral coefficients (MFCC) to extract voice features and long short-term memory (LSTM), a recurrent neural network (RNN) for classification. The said approach is validated using the Al-Quran dataset. The results demonstrate that the proposed system outperforms the state-of-the-art approaches with an accuracy rate of 97.7%. This system will help the Muslim community all over the world to recite the Al-Quran in the right way in the absence of human help due to similar future pandemics.

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“…However, the performance of SVM was found to be lower compared to that of Extreme Gradient Boosting (XGBoost) in sound classification [7], [8]. Additionally, research has explored the comparison of feature extraction between RASTA-PLP in Arabic letter recognition [9]. Durairaj conducted research by combining various extraction techniques, including LPC, MFCC, and RASTA-PLP [10], whereas Helali [11] utilized MFCC, PLP, and LPC extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the research mentioned above [6], [9]- [11], various classification models have been introduced. However, these studies primarily focus on the makhraj, or sound of the Arabic corpus, rather than the properties of the letters.…”

Section: Introductionmentioning

confidence: 99%

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad

Azis,

Lestari

2023

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According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.

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Mispronunciation Detection in Articulation Points of Arabic Letters using Machine Learning

Cited by 4 publications

References 9 publications

Quran Mobile Application: A Structured Review

Quran Mobile Application: A Structured Review

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad

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