Enhancing Automatic Recognition of Isolated Arabic Speech Using Artificial Intelligence Techniques: A Systematic Review
Enhancing Automatic Recognition of Isolated Arabic Speech Using Artificial Intelligence Techniques: A Systematic Review |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : Mithal Khaleel Ismael, Goh Chin Hock, Hazem Noori Abdulrazzak |
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| DOI : 10.14445/22315381/IJETT-V73I3P116 | ||
How to Cite?
Mithal Khaleel Ismael, Goh Chin Hock, Hazem Noori Abdulrazzak, "Enhancing Automatic Recognition of Isolated Arabic Speech Using Artificial Intelligence Techniques: A Systematic Review," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 212-229, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P116
Abstract
Automatic Speech Recognition (ASR) and spoken language systems have indeed made remarkable strides, fueled by advances in artificial intelligence, deep learning, and computational power. Modern ASR systems are now more accurate, robust, and capable of handling a variety of applications, from voice assistants to real-time transcription services. This review discusses the developments in isolated Arabic speech recognition using different AI methodologies. It emphasizes fundamental techniques, such as deep learning and machine learning algorithms, and then evaluates their effectiveness in enhancing recognition accuracy. This paper highlights the inherent obstacles of the Arabic language, including dialectal differences and phonetic complexities. It also examines the importance of feature extraction and model training in improving performance. The methodologies used in speech detection and processing, identifying gaps and correlations between known patterns, and presenting recent patterns are illustrated in this paper. A systematic review of the selected studies was conducted to identify and select relevant papers. The evaluation indicates that despite significant progress, additional research is needed to overcome current limitations and enhance practical application.
Keywords
Dialect recognition, MSA, Speech recognition, Artificial Intelligence, Machine Learning, ASR.
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