## 1 Introduction  
Dialects are variances in phonology, lexicon, and grammar shaped by cultural influences, educational backgrounds, geographical locations, and socioeconomic levels. Automatic Dialect Identification (ADI) systems seek to identify dialects from spoken utterances by analyzing speech features [1]. More than 420 million people speak Arabic, a Semitic language with an intense cultural heritage and history, particularly in the Arab world and numerous diaspora communities. The linguistic landscape of the Arab world differs significantly, even though Modern Standard Arabic (MSA) is the certified lingua franca of Arab nations and operates as a communication platform in media sources. The Arabic dialects used in colloquial speech diverge considerably compared with MSA based on phonology, morphology, and syntax. Simultaneously, MSA is the solitary structured and standardized variant of Arabic used formally in written communications [2]. A broad range of Arabic dialects has been established over the centuries, each reflecting the area's distinct history, topography, and cultural influences. These dialects' pronunciation, vocabulary, grammar, and syntax are greatly diverse; whilst they are generally intelligible, they may pose serious obstacles to cross-regional communication and comprehension. For example, a person who speaks Egyptian Arabic faces a challenge in understanding Gulf Arabic or Levantine Arabic. Dialect identification (DID) is a process that inevitably determines the appropriate dialect within spoken or transcribed Arabic language. Listeners analyze incoming acoustic-phonetic information, including a speaker's dialect or accent, which may vary from their native dialect across several dimensions of sound structure. The production patterns of non-native speakers frequently vary at all levels of phonological structure, and these discrepancies are prevalent among non-native speakers, irrespective of their native language backgrounds. Comprehending the impact of various linguistic variants on intelligibility, processing, and representation is essential. Listeners exhibit heightened sensitivity to the existence of non-native accents [3].  
Dialects are closely related forms of a common language, and language recognition is a remarkably complex task [4]. Applications such as machine translation, speech recognition, and customized user experiences are difficult to develop due to the linguistic diversity across several regions. This situation heralds the discovery of deep learning techniques for automatic Arabic dialect classification to construct more inclusive and efficient language technologies for the Arabic-speaking community. By precisely categorizing Arabic speech according to its dialectal origin, we can construct individualized language-processing systems that gather the unique requirements and preferences of speakers of several dialects. Handcrafted features, namely, prosodic (pitch, intensity, and duration), acoustic (Mel-Frequency Cepstral Coefficients, or MFCCs), and linguistic (phoneme frequencies, n-grams) features, are commonly used in traditional dialect classification methods. However, these techniques involve significant manual effort and might not adequately convey spoken language's modulation and intricate specifics. Deep learning is a feasible alternative that automatically extracts high-level interpretations from untreated audio data.  
Research Gap Researchers often overlook distinctive region-specific characteristics such as linguistic and acoustic features and vocabulary, focusing instead on broad dialect classifications (such as Egyptian and Levantine), not specific regional variations. Although class imbalances have been mentioned in a few studies, there is no systematic practice associated with dealing with imbalanced datasets from other geographical areas. Moreover, researchers have focused on publicly available datasets, and there is a dearth of research on the variations in Arabic dialects in private datasets.