SMARTAJWEED Automatic Recognition of Arabic Quranic Recitation Rules

Alagrami, Ali M.; Eljazzar, Maged M.

doi:10.5121/csit.2020.101812

Cited by 7 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [33], the authors presented an automatic model for recognizing TajweedShould we change "Tajweed" to "Tajwid" here and other instances. Please confrm, the Qur'an recitation rules.…”

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

View full text Add to dashboard Cite

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%.

show abstract

“…In [33], the authors presented an automatic model for recognizing TajweedShould we change "Tajweed" to "Tajwid" here and other instances. Please confrm, the Qur'an recitation rules.…”

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

View full text Add to dashboard Cite

show abstract

“…This model can be applied to protocol classification, feature learning, anomalous protocol identification, and unknown protocol classification. The researchers in [24] used a Support Vector Machine (SVM) and threshold scoring system to recognize different Tajweed rules automatically. 70-dimensional filter banks were used for feature extraction.…”

Section: Nahar Et Al Inmentioning

confidence: 99%

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-toend transformer-based models for the Arabic language, as the researchers' community pays little attention to it. The Muslims Holy Qur'an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur'an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model. A Mel filter bank is used for feature extraction. To build a language model (LM), the Recurrent Neural Network (RNN) and Long short-term memory (LSTM) were used to train an n-gram word-based LM. As a part of this research, a new dataset of Qur'an verses and their associated transcripts were collected and processed for training and evaluating the proposed model, consisting of 10 h of .wav recitations performed by 60 reciters. The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate (CER) of 1.98% and a word error rate (WER) of 6.16%. We have achieved state-of-the-art end-to-end transformer-based recognition for Qur'an reciters.

show abstract

“…Furthermore, researchers also explored various feature extraction techniques. For instance, [11] utilized FB for feature extraction and SVM for classification. [33] also used different methods, such as Formant Analysis, Power Spectral Density (PSD), and MFCC, along with LDA and QDA for sorting.…”

Section: Hqsr Taxonomymentioning

confidence: 99%

Speech Recognition Models for Holy Quran Recitation Based on Modern Approaches and Tajweed Rules: A Comprehensive Overview

Al-Fadhli,

Al-Harbi,

Cherif

2023

IJACSA

View full text Add to dashboard Cite

Speech is considered the most natural way to communicate with people. The purpose of speech recognition technology is to allow machines to recognize and understand human speech, enabling them to take action based on the spoken words. Speech recognition is especially useful in educational fields, as it can provide powerful automatic correction for language learning purposes. In the context of learning the Quran, it is essential for every Muslim to recite it correctly. Traditionally, this involves an expert gari who listens to the student's recitation, identifies any mistakes, and provides appropriate corrections. While effective, this method is time-consuming. To address this challenge, apps that help students fix their recitation of the Holy Quran are becoming increasingly popular. However, these apps require a robust and error-free speech recognition model. While recent advancements in speech recognition have produced highly accurate results for written and spoken Arabic and non-Arabic speech recognition, the field of Holy Quran speech recognition is still in its early stages. Therefore, this paper aims to provide a comprehensive literature review of the existing research in the field of Holy Quran speech recognition. Its goal is to identify the limitations of current works, determine future research directions, and highlight important research in the fields of spoken and written languages.

show abstract

SMARTAJWEED Automatic Recognition of Arabic Quranic Recitation Rules

Cited by 7 publications

References 8 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 End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Speech Recognition Models for Holy Quran Recitation Based on Modern Approaches and Tajweed Rules: A Comprehensive Overview

Contact Info

Product

Resources

About