Intelligent Quran Recitation Recognition and Verification: Research Trends and Open Issues

Alrumiah, Sarah S.; Al-Shargabi, Amal A.

doi:10.1007/s13369-022-07273-8

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…The easiest way is to have a native-level tutor correct any word recognition errors with you. A tutor with ASR will listen as the student reads the words and give feedback and corrective guidance if the student's pronunciation is incorrect [12]. The second usage scenario concerns improving students' learning efficiency and self-confidence.…”

Section: Application Analysismentioning

confidence: 99%

Application of Speech Recognition in English PronunciationCorrection

Zhang¹

2023

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In the context of economic globalization, countries are beginning to conduct ordinary trade transactions, so English, asthe most frequently used language, has been incorporated into the education system of each government and studentsare being taught English systematically, but the fastest way to improve English must be for a student to have a one-onone lesson with a native English speaker. However, with the world’s advanced educational resources, teaching Englishto students on a one-to-one basis is obviously not practical. Therefore, implanting a speech recognition system intoeducational software to help the software track or correct students’ English learning in real time is possible. However,replacing a real English teacher with the current speech recognition accuracy rate is still a challenge. Therefore, thispaper explains in detail how to improve speech recognition accuracy through lip motion tracking and MFCC and to givelearners the corresponding vocabulary ratings and suggestions for improvement through this model. In the long run, theknowledge tracking model is used to help students find the most suitable learning style.

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Section: Application Analysismentioning

confidence: 99%

Application of Speech Recognition in English PronunciationCorrection

Zhang¹

2023

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“…MFCC features are widely used to determine the type or error for Al-Quran word pronunciation or Tajweed rules [10]. In addition, there are further discussion on the uses of MFCC as features in Harakaat recognition or identification [11], [12] and the prosody characteristics of speech signals associated with Harakaat [13]. All this emphasizes that it has a high potential to be used to obtain important characteristics.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Time Warping Features Extraction Design for Quranic Syllable-based Harakaat Assessment

Shafie¹,

Azizan²,

Adam³

et al. 2022

IJACSA

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The use of technological speech recognition systems with a variety of approaches and techniques has grown exponentially in varieties of human-machine interaction applications. The assessment for Qur'anic recitation errors based on syllables utterance is used to meet the Tajweed rules which generally consist of Harakaat (prolonging). The digital transformation of Quranic voice signals with identification of Tajweed-based recitation errors of Harakaat is the main research work in this paper. The study focused on speech processing implemented using the representation of Quranic Recitation Speech Signals (QRSS) in the best digital format based on Al-Quran syllables and feature extraction design to reveal similarities or differences in recitation (based on Al-Quran syllables) between experts and student. The method of Dynamic Time Warping (DTW) is used as Short Time Frequency Transform (STFT) of QRSS syllable feature for Harakaat measurement. Findings from this paper include an approach based on human-guidance threshold classification that is used specifically to evaluate Harakaat based on the syllables of the Qur'an. The threshold classification performance obtained for Harakaat is above 80% in the training and testing stages. The results of the analysis at the end of the experiment have concluded that the threshold classification method for Minimum Path Cost (MPC) feature parameters can be used as an important feature to evaluate the rules of Tajwid Harakaat embedded in syllables.

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Evaluating the Potential of Artificial Intelligence in Islamic Religious Education

Abubakari,

Shafik,

Hidayatullah

2024

Advances in Educational Technologies and Instructional Design

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Artificial intelligence (AI) has become enormously prominent in various industries in recent years, completely changing the way humans accomplish their daily activities, especially the educational process. While integrating AI technologies in educational settings might present new potential, there are drawbacks and challenges as well, particularly in the Islamic religious educational context. Therefore, the purpose of this chapter is to assess the viability of incorporating AI into Islamic religious educational settings by doing a thorough SWOT (strengths, weaknesses, opportunities, and threats) analysis. The chapter explores the benefits, drawbacks, opportunities, and risks of implementing AI in the context of Islamic education. Thus, the factors, both internal and external, that may have an impact on the effective integration of AI in Islamic educational environments are evaluated. Policymakers, educators, and other educational stakeholders may make well-informed judgments on the utilization of AI technologies in Islamic religious education by recognizing these analytical aspects.

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Intelligent Quran Recitation Recognition and Verification: Research Trends and Open Issues

Cited by 3 publications

References 43 publications

Application of Speech Recognition in English PronunciationCorrection

Application of Speech Recognition in English PronunciationCorrection

Dynamic Time Warping Features Extraction Design for Quranic Syllable-based Harakaat Assessment

Evaluating the Potential of Artificial Intelligence in Islamic Religious Education

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