The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.
KeywordsAIArabic DialectCNNRNN
Wei, G. (2022) Research on Internet Text Sentiment Classification Based on BERT and CNN-BiGRU. 2022 11th International Conference on Communications, Circuits and Systems (ICCCAS), Singapore, 13-15 May 2022, 285-289. https://doi.org/10.1109/ICCCAS55266.2022.9824526
Omran, T.M., Sharef, B.T., Grosan, C. and Li, Y. (2023) Transfer Learning and Sentiment Analysis of Bahraini Dialects Sequential Text Data Using Multilingual Deep Learning Approach. Data & Knowledge Engineering, 143, Article ID: 102106. https://doi.org/10.1016/j.datak.2022.102106
Qureshi, K.N., et al. (2022) A Blockchain-Based Efficient, Secure and Anonymous Conditional Privacy-Preserving and Authentication Scheme for the Internet of Vehicles. Applied Sciences, 12, Article No. 476. https://doi.org/10.3390/app12010476
Gobert, M. (2023) Helping Gulf Arab Learners Negotiate the Linguistic Challenges Posed by English as a Medium of Instruction. In: Wyatt, M. and El Gamal, G., Eds., English as a Medium of Instruction on the Arabian Peninsula, Routledge, London. https://doi.org/10.4324/9781003183594-12
Gravano, A. (2009) Turn-Taking and Affirmative Cue Words in Task-Oriented Dialogue. Columbia University, New York.
Altowayti, W.A.H., et al. (2022) The Role of Conventional Methods and Artificial Intelligence in the Wastewater Treatment: A Comprehensive Review. Processes, 10, Article No. 1832. https://doi.org/10.3390/pr10091832
Rasool, M., Ismail, N.A., Al-Dhaqm, A., Yafooz, W.M.S. and Alsaeedi, A. (2023) A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-Tuning. Electronics, 12, Article No. 149. https://doi.org/10.3390/electronics12010149
Mohammed, M.Q., et al. (2022) Review of Learning-Based Robotic Manipulation in Cluttered Environments. Sensors, 22, Article No. 7938. https://doi.org/10.3390/s22207938
Kong, L. (2013) An Improved Information-Security Risk Assessment Algorithm for a Hybrid Model. International Journal of Advanced Computer Technology, 5.
Mohammed, M.Q., et al. (2021) Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion. Sustainability, 13, Article No. 13686. https://doi.org/10.3390/su132413686
Nagwani, N.K. and Suri, J.S. (2023) An Artificial Intelligence Framework on Software Bug Triaging, Technological Evolution, and Future Challenges: A Review. International Journal of Information Management Data Insights, 3, Article ID: 100153. https://doi.org/10.1016/j.jjimei.2022.100153
Gao, S., et al. (2023) Code Structure-Guided Transformer for Source Code Summarization. ACM Transactions on Software Engineering and Methodology, 32, 1-32. https://doi.org/10.1145/3522674
Gupta, C., Johri, I., Srinivasan, K., Hu, Y.-C., Qaisar, S.M. and Huang, K.-Y. (2022) A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks. Sensors, 22, Article No. 2017. https://doi.org/10.3390/s22052017
Moore, D.A. (2013) Security Risk Assessment Methodology for the Petroleum and Petrochemical Industries. Journal of Loss Prevention in the Process Industries, 26, 1685-1689. https://doi.org/10.1016/j.jlp.2013.10.012
Hassan, M., Saeedi, K., Almagwashi, H. and Alarifi, S. (2023) Information Security Risk Awareness Survey of Non-governmental Organization in Saudi Arabia. In: Visvizi, A., Troisi, O. and Grimaldi, M., Eds., Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity, Springer, Cham, 39-71. https://doi.org/10.1007/978-3-031-19560-0_4
Alshareef, N.M.N. (2022) Information Security Risk Management (ISRM) Model for Saudi Arabian Organisations. Curtin University, Perth.
Tuyikeze, T. and Flowerday, S. (2014) Information Security Policy Development and Implementation: A Content Analysis Approach. 8th International Symposium on Human Aspects of Information Security and Assurance, Plymouth, 8-9 July 2014, 11-20.
Habash, N., Rambow, O., Diab, M. and Kanjawi-Faraj, R. (2008) Guidelines for Annotation of Arabic Dialectness. Proceedings of the LREC Workshop on HLT & NLP within the Arabic World, Marrakech, 49-53.
Diab, M., Habash, N., Rambow, O., Altantawy, M. and Benajiba, Y. (2010) COLABA: Arabic Dialect Annotation and Processing. LREC Workshop on Semitic Language Processing, Malta, 17-23 May 2010, 66-74.
Zaidan, O. and Callison-Burch, C. (2011) The Arabic Online Commentary Dataset: An Annotated Dataset of Informal Arabic with High Dialectal Content. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, 19-24 June 2011, 37-41.
Elfardy, H. and Diab, M. (2013) Sentence Level Dialect Identification in Arabic. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, 4-9 August 2013, 456-461.
Cotterell, R. and Callison-Burch, C. (2014) A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic. 9th International Conference on Language Resources and Evaluation, Reykjavik, 26-31 May 2014, 241-245.
Tillmann, C., Mansour, S. and Al-Onaizan, Y. (2014) Improved Sentence-Level Arabic Dialect Classification. Proceedings of the 1st Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects, Dublin, 23 August 2014, 110-119. https://doi.org/10.3115/v1/W14-5313
Nasr, M., Ateia, M. and Hassan, K. (2016) Artificial Intelligence for Greywater Treatment Using Electrocoagulation Process. Separation Science and Technology, 51, 96-105. https://doi.org/10.1080/01496395.2015.1062399
Sadat, F., Kazemi, F. and Farzindar, A. (2014) Automatic Identification of Arabic Language Varieties and Dialects in Social Media. Proceedings of the 2nd Workshop on Natural Language Processing for Social Media (SocialNLP), Dublin, 24 August 2014, 22-27. https://doi.org/10.3115/v1/W14-5904
Durandin, O.V., Hilal, N.R. and Strebkov, D.Y. (2016) Automatic Arabic Dialect Classification. Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue 2016”, Moscow, 1-4 June 2016, 1-13.
Ramadan, H., Alqahtani, M. and Algoson, A. (2022) Identifying Equivalent Words from Different Arabic Dialects Using Deep Learning Techniques. 2022 20th International Conference on Language Engineering (ESOLEC), Cairo, 12-13 October 2022, 124-128. https://doi.org/10.1109/ESOLEC54569.2022.10009555
Alsayadi, H.A., Al-Hagree, S., Alqasemi, F.A. and Abdelhamid, A.A. (2022) Dialectal Arabic Speech Recognition using CNN-LSTM Based on End-to-End Deep Learning. 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA), Ibb, 25-26 October 2022, 1-8. https://doi.org/10.1109/eSmarTA56775.2022.9935427
Nasr, S., Duwairi, R. and Quwaider, M. (2023) End-to-End Speech Recognition for Arabic Dialects. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-023-07670-7
Ali, A., et al. (2015) Automatic Dialect Detection in Arabic Broadcast Speech. Interspeech 2016, San Francisco, 8-12 September 2016, 2934-2938. https://doi.org/10.21437/Interspeech.2016-1297
Alzu’bi, D. and Duwairi, R. (2021) Detecting Regional Arabic Dialect Based on Recurrent Neural Network. 2021 12th International Conference on Information and Communication Systems (ICICS), Valencia, 24-26 May 2021, 90-93. https://doi.org/10.1109/ICICS52457.2021.9464605
Elaraby, M. and Abdul-Mageed, M. (2018) Deep Models for Arabic Dialect Identification on Benchmarked Data. Proceedings of the 5th Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), Santa Fe, 20 August 2018, 263-274.
Zaidan, O.F. and Callison-Burch, C. (2014) Arabic Dialect Identification. Computational Linguistics, 40, 171-202. https://doi.org/10.1162/COLI_a_00169
Itani, M.M., Zantout, R.N., Hamandi, L. and Elkabani, I. (2012) Classifying Sentiment in Arabic Social Networks: Naive Search versus Naive Bayes. 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Beirut, 12-15 December 2012, 192-197. https://doi.org/10.1109/ICTEA.2012.6462864
Yahya, A.E., Gharbi, A., Yafooz, W.M.S. and Al-Dhaqm, A. (2023) A Novel Hybrid Deep Learning Model for Detecting and Classifying Non-Functional Requirements of Mobile Apps Issues. Electronics, 12, Article No. 1258. https://doi.org/10.3390/electronics12051258
Mounsef, J., Hasib, M. and Raza, A. (2022) Building an Arabic Dialectal Diagnostic Dataset for Healthcare. International Journal of Advanced Computer Science and Applications, 13, 859-868. https://doi.org/10.14569/IJACSA.2022.01307100
Al-Dhaqm, A., Abd Razak, S., Ikuesan, R.A., Kebande, V. R. and Siddique, K. (2020) A Review of Mobile Forensic Investigation Process Models. IEEE Access, 8, 173359-173375. https://doi.org/10.1109/ACCESS.2020.3014615
Slunečková, L. (2018) ESP Students and the Mysteries of English Word Order. In: Jančaříková, R., Ed., Interpretation of Meaning across Discourses, Masarykova Univerzita Nakladatelství, 109-120.
Ngadi, M., Al-Dhaqm, R. and Mohammed, A. (2012) Detection and Prevention of Malicious Activities on RDBMS Relational Database Management Systems. International Journal of Scientific & Engineering Research, 3, 1-10.
Zu’bi, A. (2023) Some Linguistic Features of the Dialect of Acre and Their Possible Explanation by the History of the City. Journal of Semitic Studies, Article ID: Fgac029. https://doi.org/10.1093/jss/fgac029
Saleh, M.A., Othman, S.H., Al-Dhaqm, A. and Al-Khasawneh, M.A. (2021) Common Investigation Process Model for Internet of Things Forensics. 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Cameron Highlands, 15-17 June 2021, 84-89. https://doi.org/10.1109/ICSCEE50312.2021.9498045
Al-Dhaqm, A., Razak, S., Siddique, K., Ikuesan, R.A. and Kebande, V.R. (2020) Towards the Development of an Integrated Incident Response Model for Database Forensic Investigation Field. IEEE Access, 8, 145018-145032. https://doi.org/10.1109/ACCESS.2020.3008696
Al-Dhaqm, A., et al. (2017) CDBFIP: Common Database Forensic Investigation Processes for Internet of Things. IEEE Access, 5, 24401-24416. https://doi.org/10.1109/ACCESS.2017.2762693
Al-Dhaqm, A., et al. (2020) Categorization and Organization of Database Forensic Investigation Processes. IEEE Access, 8, 112846-112858. https://doi.org/10.1109/ACCESS.2020.3000747