World Holy Quran Reciter Recognition based on deep learning
Leveraging AI for Sacred Text Analysis
This analysis delves into the innovative application of deep learning for identifying reciters of the Holy Quran, addressing crucial needs in cultural preservation and technological advancement.
Article Author: Mustafa Mhamed and Jamal Ali Noja
Publication Date: April 18-20, 2025
Quantifiable Impact of Advanced Quranic AI
Our deep learning models achieve unprecedented accuracy in reciter recognition, offering significant potential for cultural preservation and educational tools.
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Accuracy Rate
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Dataset Wave Files
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Unique Reciters
Deep Analysis & Enterprise Applications
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The core methodology involved feature extraction and deep neural network application.
Reciter Recognition Process
Collect Audio Samples
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Segment Audio (5s frames)
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Extract Features (MFCC, MelSpec)
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Apply DNN Models (VGG16)
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Evaluate Performance
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Achieve Recognition
| Model | Training Time | Accuracy |
|---|---|---|
| LSTM | 00:18:26 | 89.41% |
| ResNet50 | 01:52:35 | 92.63% |
| AlexNet | 01:29:14 | 94.52% |
| VGG16 | 01:43:14 | 97.12% |
The creation of a new dataset addresses a critical gap, alongside inherent challenges in Arabic speech processing.
3,150
Wave Files in WHORR Dataset
The newly created World Holy Quran Reciter Recognizing (WHORR) dataset focuses on chapter 36, including 3,150 wave files derived from 83 spoken words.
Addressing Data Scarcity in Arabic AI
The research highlights the significant challenge of data scarcity for Quran reciters, which has historically hindered deep learning model training. The WHORR dataset directly addresses this by providing high-quality, labeled audio data for 21 reciters, focusing on 'Yasin surah' (Chapter 36) to enable more robust model development and benchmarking. This effort is crucial for overcoming biases and improving performance across diverse phonetic features unique to each reciter.
Challenge: Lack of standardized, high-quality, and labeled datasets for prominent Quran reciters.
Solution: Creation of the World Holy Quran Reciter Recognizing (WHORR) dataset with 3,150 wave files from 21 reciters.
Impact: Enables robust deep learning model training, reduces performance bias, and improves recognition accuracy by providing diverse phonetic features.
Effective feature extraction is paramount for accurate speech recognition.
| Feature Strategy | Accuracy |
|---|---|
| FilterBank | 90.54% |
| MelSpec | 94.77% |
| Chroma | 83.23% |
| MFCC | 97.12% |
97.12%
MFCC Feature Accuracy with VGG16
MFCC feature extraction achieved the highest recognition rate of 97.12% when combined with the VGG16 deep neural network, underscoring its effectiveness for distinguishing unique phonetic features.
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