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.
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