Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System


  • A. A. Alasadi Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, India
  • T. H. Aldhayni Community College in Abqaiq, King Faisal University, Saudi Arabia
  • R. R. Deshmukh Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, India
  • A. H. Alahmadi Department of Computer Science, Taibah University, Saudi Arabia
  • A. S. Alshebami Community College in Abqaiq, King Faisal University, Saudi Arabia


This paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition System (ASR) in Arabic. The Support Vector Machine (SVM) algorithm processed the obtained features. These feature extraction algorithms extract speech or voice characteristics and process the group delay functionality calculated straight from the voice signal. These algorithms were deployed to extract audio forms from Arabic speakers. PNCC provided the best recognition results in Arabic speech in comparison with the other methods. Simulation results showed that PNCC and ModGDF were more accurate than MFCC in Arabic speech recognition.


speech recognition, feature extraction, PNCC, ModGDF, MFCC, Arabic speech recognition


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How to Cite

A. A. Alasadi, T. H. Aldhayni, R. R. Deshmukh, A. H. Alahmadi, and A. S. Alshebami, “Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 2, pp. 5547–5553, Apr. 2020.


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