An Efficient Holy Quran Recitation Recognizer Based on SVM Learning Model

Nahar, Khalid M.O.; khatib, Raed; Shannaq, Moyawiah; Barhoush, Malek

doi:10.5455/jjcit.71-1593380662

Cited by 12 publications

(7 citation statements)

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“…The results showed that ANN reached an accuracy rate up to 97.7% while KNN showed an accuracy rate of 97.03%. [19] have used a recognition model to identify the "Qira'ah" (type of reading) from the related Holy Qur'an audio wave. The proposed model was created in 3 stages: (i) the extraction and labeling of MFCC features from an acoustic signal, (ii) training the SVM learning model with the identified features, and (iii) detecting "Qira'ah".…”

Section: Related Workmentioning

confidence: 99%

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

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

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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.

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Section: Related Workmentioning

confidence: 99%

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

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

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“…In the classification step, a powerful classification algorithm is applied to determine if the image segment is a face or not. In this case, Support Vector Machine (SVM) is used [15]. Further, the Algorithms of Artificial Neural Network (ANN), and Deep Learning are widely used [16].…”

Section: A Face Detection Phasementioning

confidence: 99%

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Nahar¹,

Abul-Huda²,

bataineh³

et al. 2022

IJCDS

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Twins recognition and identification is one of the important challenges in the field of image processing. The strong similarity between identical twins makes it hard to distinguish the twin from his/her sibling. Similarities come from biometric, geometric, and photometric features. In biometric patterns, the fingerprints found to be identical in some cases, geometrically, the twins' faces rarely differ which confuses people. Photometric features are very close to each other even though they rarely success in twins' recognition. We tackle this challenge by a model for twin's face recognition (FR) where our solution is based on deep transfer learning in terms of residual neural networks including two VGG16 trained networks, which are considered to be one of the powerful and deeply learned neural networks. For comparison purposes, we check other approaches to solve the twins' problem including iris, fingerprints, and lip corners. The data used was collected from Google which is a challenge. Data contains 4-pairs of twins with the 17-different position for each one which produces 5×2×17 (170) different images. Collected images were used for comparisons between features. Results show that geometrical features gave 85% of success while photometric features gave 96%. By hybridizing geometrical and photometric features together, the results reach 98% of accuracy. Biometric measures, in this research, prove the superiority of deeply transferring learning over traditional methods. The newly achieved method could be replaced to assist authentication systems that fully depend on biometric features.

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“…Each FV in the original cluster is added to a cluster such that this FV is closer to the centroid of the joined cluster. To achieve this, D1 and D2 are computed according to (7).…”

Section: Proposed Clustering Algorithm Based On Lbg-vqmentioning

confidence: 99%

“…These numerous vocabulary counts have made the characteristics of the Arabic language different from any other language. In addition to the richness of Arabic, the Holy Quran recitation has specially formulated rules known as Tajweed [7].…”

Section: Introductionmentioning

confidence: 99%

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Al‐Jarrah¹,

Ahmad²,

Jarrah³

et al. 2022

RIA

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The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method.

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An Efficient Holy Quran Recitation Recognizer Based on SVM Learning Model

Cited by 12 publications

References 0 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

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

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