A Holy Quran Reader/Reciter Identification System Using Support Vector Machine

Nahar, Khalid M.O.; Al-Shannaq, Moy’awiah A.; Manasrah, Ahmad; Alshorman, Rafat; Alazzam, Iyad

doi:10.18178/ijmlc.2019.9.4.826

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…Te authors of [34] present a system for recognizing the identity of Qur'an reciters. Te authors selected two surahs for the dataset: Al-Baqraa and Al-Kahaf.…”

Section: Advances In Multimediamentioning

confidence: 99%

The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

Samara

Al-Daoud

Swerki

et al. 2023

Advances in Multimedia

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The Holy Qur’an has recently gained recognition in the field of speech-processing research. It is the central book of Islam, from which Muslims derive their religious teachings. The Qur’an is the primary source and highest authority for all Islamic beliefs and legislation. It is also one of the most widely memorized and recited texts around the world. Listening to and reciting the Qur’an is one of the most important daily practices for Muslims. In this study, we propose a deep learning model using convolutional neural networks (CNNs) and a dataset consisting of seven well-known reciters. We utilize mel frequency cepstral coefficients (MFCCs) to extract and evaluate information from audio sources. We compare our proposed model to different deep learning and machine learning methodologies. Our proposed model outperformed the competing models with an accuracy of 99.66%, compared to the support vector machine’s accuracy of 99%.

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“…Te authors of [34] present a system for recognizing the identity of Qur'an reciters. Te authors selected two surahs for the dataset: Al-Baqraa and Al-Kahaf.…”

Section: Advances In Multimediamentioning

confidence: 99%

The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

Samara

Al-Daoud

Swerki

et al. 2023

Advances in Multimedia

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show abstract

“…It has twentyeight consonants and three vowels. Te vowels are further classifed as short and long [4]. Tere are multiple challenges associated with Arabic speech recognition due to its rich vocabulary, multiple dialects, and diferent accents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tis makes Quranic ASR more challenging as compared to simple Arabic ASR. Moreover, the reciter of Quran can add emotions in recitation due to which the sound of phonemes transit from one acoustic level to the other [4]. Tere are seven famous recitation styles known as "Qira'at" of Al-Quran.…”

Section: Literature Reviewmentioning

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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“…There have been several research efforts in speaker identifications. Several search attempts for the Quran reciter's identification and verification are proposed, using recurrent neural networks and deep learning, see [7], [11]- [13], [13], [14].…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we aim to develop a deep learning model for voice identification in Arabic speech. We use a Quranic dataset developed by [7].…”

Section: Introductionmentioning

confidence: 99%

Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset

Moustafa¹,

Aly²

2021

Preprint

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Current authentication and trusted systems depend on classical and biometric methods to recognize or authorize users. Such methods include audio speech recognitions, eye, and finger signatures. Recent tools utilize deep learning and transformers to achieve better results. In this paper, we develop a deep learning constructed model for Arabic speakers' identification by using Wav2Vec2.0 and HuBERT audio representation learning tools. The end-to-end Wav2Vec2.0 paradigm acquires contextualized speech representations learning's by randomly masking a set of feature vectors, and then applies a transformer neural network. We employ an MLP classifier that is able to differentiate between invariant labeled classes. We show several experimental results that safeguard the high accuracy of the proposed model. The experiments ensure that an arbitrary wave signal for a certain speaker can be identified with 98% and 97.1% accuracies in the cases of Wav2Vec2.0 and HuBERT, respectively.

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A Holy Quran Reader/Reciter Identification System Using Support Vector Machine

Cited by 12 publications

References 20 publications

The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

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

Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset

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