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Intelligent Categorization of Arabic Commands Utilizing Machine Learning Techniques with Short Effective Features Vector

by Azza E.B. Ibrahim, Rana S.M. Saad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 37
Year of Publication: 2022
Authors: Azza E.B. Ibrahim, Rana S.M. Saad
10.5120/ijca2022922440

Azza E.B. Ibrahim, Rana S.M. Saad . Intelligent Categorization of Arabic Commands Utilizing Machine Learning Techniques with Short Effective Features Vector. International Journal of Computer Applications. 184, 37 ( Nov 2022), 25-32. DOI=10.5120/ijca2022922440

@article{ 10.5120/ijca2022922440,
author = { Azza E.B. Ibrahim, Rana S.M. Saad },
title = { Intelligent Categorization of Arabic Commands Utilizing Machine Learning Techniques with Short Effective Features Vector },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2022 },
volume = { 184 },
number = { 37 },
month = { Nov },
year = { 2022 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number37/32557-2022922440/ },
doi = { 10.5120/ijca2022922440 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:24.044694+05:30
%A Azza E.B. Ibrahim
%A Rana S.M. Saad
%T Intelligent Categorization of Arabic Commands Utilizing Machine Learning Techniques with Short Effective Features Vector
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 37
%P 25-32
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Different technologies are now being employed to improve the quality of life, particularly for the disabled and elderly. Speech is the quickest and most convenient method of communicating with people and technology. The majority of the works have focused on English speech; however, there is some interest in Arabic. In this study, an Arabic dataset is created, which will eventually be used to control a mobile assistant robot. Arabic is a challenging language to learn because of its many dialects, each of which has its own impact on the spoken word. The Egyptian Arabic Speech Commands (EASC) dataset was compiled from people of different backgrounds, ages, and genders who spoke in colloquial dialects. The Arabic recognition test is made more difficult by this fluctuation. Using various machine learning techniques, Arabic speech commands were classified. Mel Frequency Cepstral Coefficients were used to create an effective feature vector (MFCC). Spectral centroids and signal power are combined with MFCC to generate an enlarged features vector, which improves recognition accuracy. Because these commands will drive a robot in real time, they must be classified quickly. As a result, the training features vector's dimension is lowered by performing some statistical calculations on it. Support Vector Machines (SVM), Random Forest decision tree (RFT), Neural Network (Multi-Layer Perceptron, MLP), and k-nearest neighbors (KNN) approaches were employed as intelligent classifiers. A thorough examination of the classifiers' various parameters was carried out. With a classification accuracy of 94.84 percent, the SVM approach outperformed other techniques. We concluded in this research that the enlarged features vectors with a lower dimension are more effective for this problem and can be employed in real applications.

References
  1. Mohan BS, Nambiar V (2020) COVID-19: An Insight into SARS-CoV-2 Pandemic Originated at Wuhan City in Hubei Province of China. J Infect Dis Epidemiol 6:146. doi.org/10.23937/2474-3658/1510146
  2. RS Mahla, LB Dustin, “Lessons from a large-scale COVID-19 vaccine trial “, The Journal of Clinical Investigation, https://doi.org/10.1172/JCI163202.
  3. Fauci, Anthony S. "The story behind COVID-19 vaccines." (2021): 109-109
  4. Pfefferbaum, Betty, and Carol S. North. "Mental health and the Covid-19 pandemic." New England Journal of Medicine 383.6 (2020): 510-512.‏
  5. Di Lallo, Antonio, et al. "Medical Robots for Infectious Diseases: Lessons and Challenges from the COVID-19 Pandemic." IEEE Robotics & Automation Magazine 28.1 (2021): 18-27.‏
  6. Vindegaard, Nina, and Michael Eriksen Benros. "COVID-19 pandemic and mental health consequences: Systematic review of the current evidence." Brain, behavior, and immunity 89 (2020): 531-542.‏
  7. Islam, Mohammad Shamim, et al. "Design of A Social Robot Interact with Artificial Intelligence by Versatile Control Systems." IEEE Sensors Journal (2021).‏
  8. Al-Wesabi, Fahd N., et al. "Smart Mind-based Approach to Control Wheelchair Wirelessly." Sensors and Materials 32.4 (2020): 1533-1555.‏
  9. Korayem, M. H., et al. "Design and Implementation of the Voice Command Recognition and the Sound Source Localization System for Human–Robot Interaction." Robotica: 1-12.‏
  10. Hassan, Hani S., and S. Jammila Harbi. "Arabic Command Based Human Computer Interaction." Journal of Physics: Conference Series. Vol. 1530. No. 1. IOP Publishing, 2020.‏
  11. Qidwai, Uvais, and Mohamed Shakir. "Ubiquitous Arabic voice control device to assist people with disabilities." 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012). Vol. 1. IEEE, 2012.‏
  12. Lukas Grasse, Sylvain J. Boutros, and Matthew S. Tata, “Speech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios”, Published online 2021 Apr 8., PMCID: PMC8060474, doi: 10.3389/frobt.2021.612750.
  13. UvaisQidwai; Fatma Ibrahim, “Arabic speech-controlled wheelchair: A fuzzy scenario”, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010) DOI:10.1109/ISSPA.2010.5605493.
  14. Alasadi, Abdulmalik A., et al. "Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System." Eng. Technol. Appl. Sci. Res 10.2 (2020): 5547-5553.
  15. Deshwal, Deepti, Pardeep Sangwan, and Divya Kumar. "A language identification system using hybrid features and back-propagation neural network." Applied Acoustics 164 (2020): 107289.‏
  16. Marlina, Lina, et al. "Makhraj recognition of Hijaiyah letter for children based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) method." 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018.‏
  17. Al-Omari, Ayoub Abdelrahman. A Comparative Study of Classification Techniques for English to Arabic Speech Recognition. Diss. Middle East University, 2016.‏
  18. LubnaEljawad, Rami Aljamaeen, Mutasem K. Alsmadi, Ibrahim Al-Marashdeh, HayamAbouelmagd, Sanaa Alsmadi, Firas Haddad, Raed A. Alkhasawneh, Mohmmed Alzughoul and 5Malik B. Alazzam, “ Arabic Voice Recognition Using Fuzzy Logic and Neural Network” , International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 3 (2019) pp. 651-662 © Research India Publications. http://www.ripublication.com
  19. Elvira SukmaWahyuni, “Arabic speech recognition using MFCC feature extraction and ANN classification” , 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). DOI: 10.1109/ICITISEE.2017.8285499.
  20. Elvira SukmaWahyuni “Arabic Speech Recognition Using MFCC Feature Extraction and ANN Classification. “ , 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 978-1-5386-0658-2/17/$31.00 ©2017 IEEE.
  21. https://www.cs.cmu.edu/~music/icm/slides/05-algorithmic-composition.pdf.
  22. Jia Min Karen Kua, TharmarajahThiruvaran ,MohaddesehNosratighodsEliathambyAmbikairajah, Julien Epps, “Investigation of Spectral Centroid Magnitude and Frequency for Speaker Recognition “, Odyssey 2010 The Speaker and Language Recognition Workshop 28 June – 1 July 2010, Brno, Czech Republic. file:///C:/Users/azza/Downloads/InvestigationofSpectralCentroidMagnitudeandFrequencyforSpeakerRecognition.pdf
  23. Bayraktar, R., Akgul, B. A. & Bayram, K. S. (2020). Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier. New Trends and Issues Proceedings on Advances in Pure and Applied Sciences. (12), 08–14. DOI: 10.18844/gjpaas.v0i12.4981.
  24. Ur¨unDo˘gan, Tobias Glasmachers, and Christian Igel. “A Unified View on Multi-class Support Vector Classification “, Journal of Machine Learning Research 17 (2016) 1-32.
  25. Muhammad Achirul Nanda, KudangBoro Seminar, Dodi Nandika, and AkhiruddinMaddu “A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection’, Information 2018, 9, 5; doi:10.3390/info9010005.
  26. Khalid AA Abakar&chongwen Yu, “ Performance of SVM based on PUK in comparison to SVM based on RBF kernel in prediction of yarn tenacity“, Indian Journal of Fibre& Textile Research, Vol.39, March 2014, pp. 55-59.
  27. Santosh Chapaneri, Renia Lopes, and DeepakeJayaswel “Evaluation of Music Features for PUK Kernel based Genre Classification.”, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
  28. A. Mathur; G. M. Foody, “Multiclass and Binary SVM Classification: Implications for Training and Classification Users “, IEEE Geoscience and Remote Sensing Letters (Volume: 5 , Issue: 2 , April 2008 ), DOI: 10.1109/LGRS.2008.915597.
  29. HanyangZhong; Xin Song; Lei Yang “Vessel Classification from Space-based AIS Data Using Random Forest”, 2019 5th International Conference on Big Data and Information Analytics (BigDIA), DOI: 10.1109/BigDIA.2019.8802792.
  30. Mohammed BEN NASR,& Mourad TALBI, AdnaneCHERIF, “Arabic Speech Recognition by MFCC and Bionic Wavelet Transform using a Multi-Layer Perceptron for Voice Control”, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).
Index Terms

Computer Science
Information Sciences

Keywords

Machine-Learning techniques (SVM) KNN MLP Random Forest Trees MFCC

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