Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm (original) (raw)
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Development of Quranic Reciter Identification System using MFCC and GMM Classifier
International Journal of Electrical and Computer Engineering (IJECE), 2018
Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system using Mel-frequency Cepstral Coefficient (MFCC) and Gaussian Mixture Model (GMM). In this paper, a database of five Quranic reciters is developed and used in training and testing phases. We carefully randomized the database from various surah in the Quran so that the proposed system will not prone to the recited verses but only to the reciter. Around 15 Quranic audio samples from 5 reciters were collected and randomized, in which 10 samples were used for training the GMM and 5 samples were used for testing. Results showed that our proposed system has 100% recognition rate for the five reciters tested. Even when tested with unknown samples, the proposed system is able to reject it.
Quranic Reciter Recognition: A Machine Learning Approach
Advances in Science, Technology and Engineering Systems Journal
Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari thus represents the 12-class problem. Two approaches are used for audio representation, firstly, the audio is analyzed in the frequency domain, and secondly, the audio is treated as images through Spectrogram. The Mel Frequency Cepstral Coefficients (MFCC) and Pitch are used as the features for model learning in the first case. In the second case of audio as images, Auto-correlograms are used to extract features. In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest. These classifiers are selected due to their overall good performance in the state-of-the-art. It is observed that classifiers can efficiently learn the separation between classes, when the audio is represented by the MFCC, and the Pitch features. In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.
Development of Quran Reciter Identification System Using MFCC and Neural Network
Indonesian Journal of Electrical Engineering and Computer Science, 2016
Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently, 91.2% of the successful match between targets and input occurred with certain number of hidden layers which shows how efficient are Mel Frequency Cepstral Coef...
Speaker Independent Quranic Recognizer Basedon Maximum Likelihood Linear Regression
2007
An automatic speech recognition system for the formal Arabic language is needed. The Quran is the most formal spoken book in Arabic, it is spoken all over the world. In this research, an automatic speech recognizer for Quranic based speakerindependent was developed and tested. The system was developed based on the tri-phone Hidden Markov Model and Maximum Likelihood Linear Regression (MLLR). The MLLR computes a set of transformations which reduces the mismatch between an initial model set and the adaptation data. It uses the regression class tree, as well as, estimates a set of linear transformations for the mean and variance parameters of a Gaussian mixture HMM system. The 30th Chapter of the Quran, with five of the most famous readers of the Quran, was used for the training and testing of the data. The chapter includes about 2000 distinct words. The advantages of using the Quranic verses as the database in this developed recognizer are the uniqueness of the words and the high leve...
Strategies for Implementing an Optimal ASR System for Quranic Recitation Recognition
With the help of automatic speech recognition (ASR) techniques, computers become capable of recognizing speech. The Quran is the speech of Allah (The God); it is the Holy book for all Muslims in the world; it is written and recited in Classical Arabic language, the language in which it was revealed by Allah to the Prophet Muhammad. Knowing how to pronounce correctly the Quranic sounds and correct mistakes occurred in reading is one of the most important topics in Quranic ASR applications, which assist self-learning, memorizing and checking the Holy Quran recitations. This paper presents a practical framework for development and implementation of an optimal ASR system for Quranic sounds recognition. The system uses the statistical approach of Hidden Markov Models (HMMs) for modeling the Quranic sounds and the Cambridge HTK tools as a development environment. Since sounds duration is regarded as a distinguishing factor in Quranic recitation and discrimination between certain Quranic sounds relies heavily on their durations, we have proposed and tested various strategies for modeling the Quranic sounds' durations in order to increase the ability in distinguishing them properly and thus enhancing their overall recognition accuracy. Experiments have been carried out on a particular Quranic Corpus containing ten male speakers and more than eight hours of speech collected from recitations of the Holy Quran. The implemented system reached (99%) as average recognition rate; which reflects its robustness and performance.
2014 IEEE Student Conference on Research and Development, 2014
This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed for used in feature extraction of Quranic accents. Through implementing the Spectral Centroid Feature, it complements in improving the accuracy result of identifying the Quranic accents. The pattern classification algorithm here used the dimensional reduced technique from Probabilistic Principal Component Analysis (PPCA) on the features and Gaussian Mixture Model, in purpose to model the effectiveness of both combination of feature extraction. The accuracy of automatic identification for such Quranic Accents are found increasing from 96.9% to 100% with the application of SCF.
Verification System for Quran Recitation Recordings
International Journal of Computer Applications, 2017
Quran is the holy book of Allah which was revealed to Prophet Mohammed. Quran is written and recited in Arabic language, the language in which it was revealed. Muslims believe that the Quran is neither corrupted nor altered this is mainly due to maintaining its original text. The Quran should be recited in Arabic language as it is with neither additions nor subtractions. When the Arabs started to mix with the non Arabs as Islam spread, mistakes in Quran recitation started to appear, so the scholars had to record the rules of tajweed and write them down in order to preserve the Qur'an recitation as revealed by Allah. In this regard, it is necessary to preserve the authenticity and integrity of the Quran from all sorts of corruption or deletion. This paper provides an overview of the techniques used in voice recognition in the Quran recitation focusing on the techniques used, the advantages, and drawbacks. And proposed model of verification system for Quran verses.
Computer speech recognition to text for recite Holy Quran
IOP Conference Series: Materials Science and Engineering
Memorizing Holy Quran or Tahfidz is important to worship for Muslim around the world. This research proposed a solution in memorizing and learning Holy Quran easily. To help in remembering the sentence of Holy Quran, Fisher-Yates Shuffle had implemented for randomization of the letter of the Holy Quran. In this research, the sound of Holy Quran had recorded and it was converted into Arabic text to recognize the character of text. Jaro-Winkler was used for text matching algorithm, and Google Speech API help to define speech recognition. The result showed that Fisher-Yates Shuffle Algorithm was successfully applied in randomization with 15 times of experiments. And also, Jaro-Winkler Distance algorithm had performed well as text matching between text from speech recognition and Holy Quran text. The result showed that the percentage of accuracy was around 91% and an average of matching time was 1.9 ms.
Quranic Verse Recitation Feature Extraction Using Mel-Frequency Cepstral Coefficient (MFCC)
Each person's voice is different. Thus, the Quran sound, which had been recited by most of recitors will probably tend to differ a lot from one person to another. Although those Quranic sentence were particularly taken from the same verse, but the way of the sentence in Al-Quran been recited or delivered may be different. It may produce the difference sounds for the different recitors. Those same combinations of letters may be pronounced differently due to the use of harakates. This paper explores the viability of Mel-Frequency Cepstral Coefficient (MFCC) technique to extract features from Quranic verse recitation. Features extraction is crucial to prepare data for classification process. MFCC is one of the most popular feature extraction techniques used in speech recognition, whereby it is based on the frequency domain of Mel scale for human ear scale. MFCCs consist of preprocessing, framing, windowing, DFT, Mel Filterbank, Logarithm and Inverse DFT.
K-mean Clustering and Arabic Vowels Formants Based Speaker Identification System
2010
This paper introduces and addresses the proposes of a new approach for speaker feature extraction based on experimental and theoretical approached, where the formants of Arabic Vowels are proposed to distinguish the speaker features from each other. Discrete Wavelet Transform (DWT) in conjunction with Algorithmic Power Spectrum Density (PSD) is used to illustrate the distinguisher of different speaker formants. This approach provides a more efficient method in speaker recognition rate, i.e., higher accuracy. Kmeans clustering (KC) and Root Mean Square Difference Similarity Measure (RDSM) are used for features classification. Instead the conventional method extracts the features from one word or more. In this paper the authors proposed a new method to utilize the Arabic Vowels. Ultimately, the attained results by the presented method showed considered a performance in classification, which reaches about 94% in classification rate. As a result of DWT utilization, the system works with...