## 7 Conclusions  
This study offers a robust approach to the automatic identification of Arabic dialects using a deep learning model. The proposed system demonstrates promising results, accurately classifying Arabic audio clips into their respective dialects. The pre-trained algorithm for speech classification can identify and categorize a variety of Arabic dialects, including EGY, GLF, LAV, MSA, and NOR. This study used MFCC features to categorize Arabic dialects and examined the effects of feature selection techniques such as ANOVA, Kendall's Tau, Spearman's correlation, and RFE and data augmentation techniques such as ADASYN, RandomOverSampler, and SMOTE. Based on the results, CNN models consistently outperformed ANN models, with CNNs and random oversampling giving the highest accuracy. Key features that are essential for dialect discrimination were consistently found through feature selection (2, 3, 4, 6, 7, and 9). The 1D CNN model achieved a higher testing accuracy of 94.28% and a validation accuracy of 95.55% using random oversampling. The Matthews correlation coefficient, precision, recall, and F1-score were also used as evaluation metrics to identify the best model. Applications such as speech-to-text transcription and personalized language learning are made possible by these findings, emphasizing the significance of taking data augmentation and feature selection into account when creating reliable and accurate Arabic dialect classification systems.  
Future Scope  
Future studies may incorporate additional features, refine the model architecture, and expand the dataset to further enhance the system's performance. Different augmentation techniques can also be used for data with multiclass problems. The dataset can be used for classification, and various techniques can be used to enhance the dialects from all five Arabic regions--MSA, EGY, LAV, NOR, and GLF. Furthermore, this model can be employed to evaluate emotions in Arabic audio data.  
Finally, the current model focused on the five main Arabic dialects--MSA, Egyptian, Gulf, Levantine, and North African--based on the available data. Increasing the model's dialectal coverage to incorporate underrepresented and lower-resource Arabic dialects, such as Mauritanian, Bahraini, Sudanese, and others, is a crucial avenue for future research.  
[PERSON] formulated the study and designed the experimental methodology. [PERSON] was also responsible for collecting all experimental data. [PERSON] performed the data analysis under [PERSON] supervision and contributed to the interpretation of the results. The initial draft of the manuscript was written by [PERSON] and [PERSON] provided critical revisions and contributed to the final  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Dataset** & **Model** & **Accuracy** \\ \hline Neural Network & & & & \\ Architectures for Arabic & 2018 & ADI dataset & CNN & 52.00\% \\ Dialect Identification [4] & & & & \\ Automatic Dialect & & Multi-dialect Arabic & & \\ identification of Spoken & 2022 & speech parallel & RNN-CNN & 62.75\% \\ Arabic Speech using Deep & & corpus & & \\ Neural Networks [12] & & & & \\ Using Transformers to & 2024 & Social network & BERT and DistilBERT & BERT = 0.85 \\ Classify Arabic Dialects [6] & & & & \\ Method Proposed in & & Own dataset & mOverSampler & 95.50\% \\ this Study & & & augmentation & \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._version of the manuscript. F.S.A. secured funding for this research and provided essential resources for its completion. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Kuwait University project number EO 07/23.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy reasons.  
The authors acknowledge the support provided by Kuwait University project number [EO 07/23], which contributed to different aspects of this research.  
The authors declare no conflicts of interest.