## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume
However, to further assess the generalization capabilities, future work should test the model on an independent dataset. Expanding the dataset to include real-world speech samples from a variety of sources, such as online repositories and spontaneous speech recordings, will further enhance the robustness of the model. In addition, domain adaptation techniques could be explored to improve model performance when transitioning between datasets with different characteristics. Further work could investigate additional augmentation methods, such as pitch shifting or advanced signal processing techniques, to further enhance model performance. Additionally, extending the scope to real-world datasets and conducting cross-dialect assessments could provide invaluable insights into the generalizability and scalability of the proposed methodologies. These findings offer practical guidance for researchers and practitioners in developing more robust and accurate SER systems, particularly for dialect-specific applications. Furthermore, the investigation of hybrid architectures, such as CNN-LSTM or CNN-GRU models, combined with data augmentation, could provide further insights into improving both feature extraction and temporal modeling for Arabic SER. These advances will contribute to the development of more generalized and practical emotion recognition systems, enhancing their applicability in human-computer interaction, mental health assessment, and intelligent speech-based applications. While this study focused on the Saudi Dialect dataset and the BAVED dataset, future work will evaluate the model on other Arabic dialects (e.g., Egyptian, Levantine, and Maghrebi Arabic) to assess its generalizability. Additionally, domain adaptation and transfer learning will be explored to enhance cross-dialect performance with minimal retraining. Expanding the dataset and testing in cross-dialect settings will provide further insights into the model's robustness and scalability. Conceptualization, W.B. and R.A.-D.; methodology, W.B. and R.A.-D.; software, W.B. and R.A.-D.; validation, W.B. and R.A.-D.; formal analysis, W.B. and R.A.-D.; investigation, W.B. and R.A.-D.; resources, W.B. and R.A.-D.; data curation, W.B. and R.A.-D.; writing--original draft preparation, W.B. and R.A.-D.; writing--review and editing, W.B., R.A.-D. and A.A.; visualization, W.B., R.A.-D. and A.A.; supervision, A.A. All authors have read and agreed to the published version of the manuscript. This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2501). ## References

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