An Automatic Qari Recognition System

Hussaini, Mohammed Abdullah; Aldhaheri, Rabah W.

doi:10.1109/acsat.2012.48

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Preprocessing, framing, windowing, DFT, Mel Filterbank, Logarithm, and Discrete Cosine Transform DCT are all part of MFCCs. An autonomous reciter recognition system based on text-independent speaker recognition approach employing MFCC is introduced by [28]. The dataset utilized a sample of 200 recordings made by 20 reciters.…”

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

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

show abstract

“…Bezoui et al [7] introduced the Quranic verses MFCCs features using MFCC. Hussaini et al [8] presented an automatic reciter recognition system using the MFCC with a text-independent speaker recognition technique. By using their own dataset of 20 reciters, they achieved an accuracy of 86.5%.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for Recognizing the Holy Quran Reciter

AlKhateeb¹

2020

IJACSA

View full text Add to dashboard Cite

Mainly, the holy Quran is the holy book for all Muslims. Reading the holy Quran is a special reading with rules. Reading the Holy Quran is called recitation. One of the Muslim essential activities is reading or listening to the Holy Quran. In this paper, a machine learning approach for recognizing the reader of the holy Quran (reciter) is proposed. The proposed system contains basic traditional phases for a recognition system, including data acquisition, pre-processing, feature extraction, and classification. A dataset is created for ten well-known reciters. The reciters are the prayer leaders in the holy mosques in Mecca and Madinah. The audio dataset set is analyzed using the Mel Frequency Cepstral Coefficients (MFCC). Both the K nearest neighbor (KNN) classifier, and the artificial neural network (ANN) classifier are applied for classification purpose. The pitch is used as features which are utilized to train the ANN and the KNN for classification. Two chapters from the Holy Quran are selected in this paper for system validation. Excellent accuracy is achieved. Using the ANN, the proposed system gives 97.62% accuracy for chapter 18 and 96.7% accuracy for chapter 36. On the other hand, the proposed system gives 97.03% accuracy for chapter 18 and 96.08% accuracy for chapter 36 by using the KNN.

show abstract

An Automatic Qari Recognition System

Cited by 2 publications

References 11 publications

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

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

A Machine Learning Approach for Recognizing the Holy Quran Reciter

Contact Info

Product

Resources

About