M.A.M. Abushariah

University of Malaya, Kuala Lumpor, Kuala Lumpur, Malaysia

Are you M.A.M. Abushariah?

Claim your profile

Publications (6)0 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents the design, implementation, and evaluation of a research work for developing an automatic person identification system using voice biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment. To extract features from voice signals, Mel-Frequency Cepstral Coefficients (MFCC) technique was applied producing a set of feature vectors. Subsequently, the system uses the Vector Quantization (VQ) for features training and classification. In order to train and test the developed automatic person identification system, an in-house voice database is created, which contains recordings of 100 persons' usernames (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 utterances are collected. This paper also investigates the effect of the persons' gender on the overall performance of the system. The voice data collected from female persons outperformed those collected from the male persons, whereby the system obtained average recognition rates of 94.20% and 91.00% for female and male persons, respectively. Overall, the voice based system obtained an average recognition rate of 92.60% for all persons.
    Computer and Communication Engineering (ICCCE), 2012 International Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment.. In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.
    Computer and Communication Engineering (ICCCE), 2012 International Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the preparation, recording, analyzing, and evaluation of a new speech corpus for Modern Standard Arabic (MSA). The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing three major regions (Levant, Gulf, and Africa). Three hundred and sixty seven sentences are considered as phonetically rich and balanced, which are used for training Arabic Automatic Speech Recognition (ASR) systems. The rich characteristic is in the sense that it must contain all phonemes of Arabic language, whereas the balanced characteristic is in the sense that it must preserve the phonetic distribution of Arabic language. The remaining 48 sentences are created for testing purposes, which are mostly foreign to the training sentences and there are hardly any similarities in words. In order to evaluate the speech corpus, Arabic ASR systems were developed using the Carnegie Mellon University (CMU) Sphinx 3 tools at both training and testing/decoding levels. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 8 h of training speech data, the acoustic model is best using continuous observation's probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains uni-grams, bi-grams, and tri-grams. For same speakers with different sentences, Arabic ASR systems obtained average Word Error Rate (WER) of 9.70%. For different speakers with same sentences, Arabic ASR systems obtained average WER of 4.58%, whereas for different speakers with different sentences, Arabic ASR systems obtained average WER of 12.39%. © 2011 Springer Science+Business Media B.V.
    Language Resources and Evaluation. 01/2011;
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper reports the design, implementation, and evaluation of a research work for developing a high performance natural speaker-independent Arabic continuous speech recognition system. It aims to explore the usefulness and success of a newly developed speech corpus, which is phonetically rich and balanced, presenting a competitive approach towards the development of an Arabic ASR system as compared to the state-of-the-art Arabic ASR researches. The developed Arabic AS R mainly used the Carnegie Mellon University (CMU) Sphinx tools together with the Cambridge HTK tools. To extract features from speech signals, Mel-Frequency Cepstral Coefficients (MFCC) technique was applied producing a set of feature vectors. Subsequently, the system uses five-state Hidden Markov Models (HMM) with three emitting states for tri-phone acoustic modeling. The emission probability distribution of the states was best using continuous density 16 Gaussian mixture distributions. The state distributions were tied to 500 senons. The language model contains uni-grams, bi-grams, and tri-grams. The system was trained on 7.0 hours of phonetically rich and balanced Arabic speech corpus and tested on another one hour. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers but similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a Word Error Rate (WER) of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a Word Error Rate (WER) of 15.59% and 14.44% with and without diacritical marks respectively.
    Computer and Communication Engineering (ICCCE), 2010 International Conference on; 06/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: Lack of spoken and written training data is one o f the main issues encountered by Arabic automatic speech recognition (ASR) researchers. Almost all written and spoken corpora are not readily available to the public and many of them can only be obtained by purchasing from the Linguistic Data Consortium (LDC) or the European Language Resource Association (ELRA). There is more shortage of spoken training data as compared to written training data resulting in a great need for more speech corpora in order to serve different domains of Arabic ASR. The available spoken corpora were mainly collected from broadcast news (radios and televisions), and telephone conversations having certain technical and quality shortcomings. In order to produce a robust speaker-independent continuous automatic Arabic speech recognizer, a set of speech recordings that are rich and balanced is required. The rich characteristic is in the sense that it must contain all the phonemes of Arabic language. It must be balanced in preserving the phonetics distribution of Arabic language too. This set of speech recordings must be based on a proper written set of sentences and phrases created by experts. Therefore, it is crucial to create a high quality written (text) set of the sentences and phrases before recording them. This work adds a new kind of possible speech data for Arabic language based text and speech applications besides other kinds such as broadcast news and telephone conversations. Therefore, this work is an invitation to all Arabic ASR developers and research groups to explore and capitalize.
    Computer and Communication Engineering (ICCCE), 2010 International Conference on; 06/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: Conference code: 81802, Export Date: 30 November 2012, Source: Scopus, Art. No.: 5556819, doi: 10.1109/ICCCE.2010.5556819, Language of Original Document: English, Correspondence Address: Abushariah, A. A. M.; Electrical and Computer Engineering Department, Faculty of Engineering, International Islamic University Malaysia, Gombak, 53100 Kuala Lumpur, Malaysia; email: ahmad2010@hotmail.com, References: (1998), www.dragonmedical-transcription.com/historyspeechrecognition.html, Garfinkel, Retrieved on 10th February 2009Forsberg, M., (2003) Why Is Speech Recognition Difficult?, , Department of Computing Science, Chalmers University of Technology, Gothenburg, Sweden;
    International Conference on Computer and Communication Engineering, ICCCE'10; 01/2010