Region-Wise Recognition and Classification of Arabic Dialects and Vocabulary: A Deep Learning Approach
Fawaz S. Al–Anzi, Bibin Shalini Sundaram Thankaleela · Applied Sciences · 2025
This article presents a unique approach to Arabic dialect identification using a pre-trained speech classification model. The system categorizes Arabic audio clips into their respective dialects by employing 1D and 2D convolutional neural network technologies built from diverse dialects from the Arab region using deep learning models. Its objective is to enhance traditional linguistic handling and speech technology by accurately classifying Arabic audio clips into their corresponding dialects. The techniques involved include record gathering, preprocessing, feature extraction, prototypical architecture, and assessment metrics. The algorithm distinguishes various Arabic dialects, such as A (Arab nation authorized dialectal), EGY (Egyptian Arabic), GLF (Gulf Arabic), LAV and LF (Levantine Arabic, spoken in Syria, Lebanon, and Jordan), MSA (Modern Standard Arabic), NOR (North African Arabic), and SA (Saudi Arabic). Experimental results demonstrate the efficiency of the proposed approach in accurately determining diverse Arabic dialects, achieving a testing accuracy of 94.28% and a validation accuracy of 95.55%, surpassing traditional machine learning models such as Random Forest and SVM and advanced erudition models such as CNN and CNN2D.