Analysis and implementation of a "Quranic" verses delimitation system in audio files using speech recognition techniques

Hassan, Taimur; Falou, Wassim El; Monla, B.

doi:10.1109/ictta.2006.1684889

Cited by 30 publications

(20 citation statements)

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“…At this point some relevant and important contributions in this area are mentioned. In [7], speaker independent THQ recognition system has been proposed, which is based on a chapter of THQ. It is developed on SPHINX-IV framework and used ML approach for acoustic model training.…”

Section: Classical Arabic Asrsmentioning

confidence: 99%

Discriminative Training for Phonetic Recognition of the Holy Quran

2015

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This paper presents the development of Holy Quran recitation recognizer. The decoder of recognizer performs sub-word level recognition at phoneme. The paper demonstrates high recognition accuracies achieved by applying incremental refinements to the HMM models of the phonemes during the training stage. The Maximumlikelihood (ML) criterion is first applied for HMMs parameter estimation, which produces average recognition accuracies of up to 83 %. This is followed by discriminative technique of minimum phone error (MPE), which is applied to minimize recognition error at phoneme level. Investigation shows that MPE based acoustic models improve generalization. The results show 3-4 % improvement in recognition accuracies, which are promising when compared with the case of ML approach applied alone.

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Section: Classical Arabic Asrsmentioning

confidence: 99%

Discriminative Training for Phonetic Recognition of the Holy Quran

2015

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“…In [1], the authors declare classification and recognition as future work. The work of [2] develops an autonomous delimiter that performs the extraction of Quranic verses from the tilawat by using the Sphinx framework. [2] only uses Surah "Al-Ikhlass" for analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The work of [2] develops an autonomous delimiter that performs the extraction of Quranic verses from the tilawat by using the Sphinx framework. [2] only uses Surah "Al-Ikhlass" for analysis. There are many limitations in [2], not only because of the sample but also due to the usage of the Hidden Markov Model (HMM).…”

Section: Introductionmentioning

confidence: 99%

Quranic Reciter Recognition: A Machine Learning Approach

Khan¹,

Qamar²,

Hadwan³

2019

Adv. sci. technol. eng. syst. j.

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Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari thus represents the 12-class problem. Two approaches are used for audio representation, firstly, the audio is analyzed in the frequency domain, and secondly, the audio is treated as images through Spectrogram. The Mel Frequency Cepstral Coefficients (MFCC) and Pitch are used as the features for model learning in the first case. In the second case of audio as images, Auto-correlograms are used to extract features. In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest. These classifiers are selected due to their overall good performance in the state-of-the-art. It is observed that classifiers can efficiently learn the separation between classes, when the audio is represented by the MFCC, and the Pitch features. In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.

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“…Another word to describe the recitation of Quran is Tarteel. While obeying to the same laws of tajweed, Tarteel is generally identified by a faster reading in contrast with tajweed where reading is in slower way and there is more focus on the artistic aspect of reading the holy Quran [14]. Creating a computer aided system for automatic learning f the holy Quran recitation is practical way of learning because it is very difficult to provide human teacher for each learner or group of learners.…”

Section: Introductionmentioning

confidence: 99%

Verification System for Quran Recitation Recordings

Ahmed¹,

Abdo²

2017

IJCA

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Quran is the holy book of Allah which was revealed to Prophet Mohammed.Quran is written and recited in Arabic language, the language in which it was revealed.Muslims believe that the Quran is neither corrupted nor altered this is mainly due to maintaining its original text.The Quran should be recited in Arabic language as it is with neither additions nor subtractions.When the Arabs started to mix with the non Arabs as Islam spread, mistakes in Quran recitation started to appear, so the scholars had to record the rules of tajweed and write them down in order to preserve the Qur'an recitation as revealed by Allah. In this regard, it is necessary to preserve the authenticity and integrity of the Quran from all sorts of corruption or deletion. This paper provides an overview of the techniques used in voice recognition in the Quran recitation focusing on the techniques used, the advantages, and drawbacks. And proposed model of verification system for Quran verses.

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Analysis and implementation of a "Quranic" verses delimitation system in audio files using speech recognition techniques

Cited by 30 publications

References 0 publications

Discriminative Training for Phonetic Recognition of the Holy Quran

Discriminative Training for Phonetic Recognition of the Holy Quran

Quranic Reciter Recognition: A Machine Learning Approach

Verification System for Quran Recitation Recordings

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