## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume
This finding suggests that the impact of augmentation techniques is more pronounced in datasets that present greater challenges in terms of balance or representation, reinforcing the need for augmentation strategies tailored to the characteristics of each dataset. ### Comparative Study

The results of this study demonstrate substantial improvements over previous works that utilized the BAVED and Saudi Dialect datasets for speech emotion recognition (SER). For the Saudi Dialect dataset, where prior studies [10] faced challenges due to the dataset's limited size and imbalance, baseline models without augmentation often achieved accuracies below 77.14%. Our findings demonstrate a substantial improvement, with the best results, obtained using the combination of volume increase and volume decrease, yielding an accuracy of 96.81% and an F1-score of 96.76%. Compared to previous works that utilized traditional feature extraction methods or simpler classifiers, our results showcase the superior performance of CNNs when combined with appropriate augmentation techniques. For the BAVED dataset, prior studies such as [24] reported accuracies ranging between 84% and 89%, depending on the features and models used. In contrast, our approach, which incorporates three data augmentation techniques--noise addition, volume increase, and volume decrease--achieved a significantly higher accuracy of 92.60% and an F1-score of 92.63%, demonstrating the effectiveness of our augmentation strategy in enhancing CNN performance. These findings validate the importance of augmentation in addressing dataset limitations and emphasize the potential of our approach for advancing Arabic SER systems. As shown in Table 4, a comparison with the state-of-the-art methods demonstrates that our model achieved superior performance over existing research. ## 6 Conclusions

This study investigated the impact of data augmentation techniques on the performance of a speech emotion recognition (SER) model for the Saudi Dialect and BAVED datasets. A systematic approach was employed to evaluate the impact of single and combined augmentations, including noise addition, volume increase, volume decrease, and time \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline Saudi Dialect SER [10] & 77.14 \\ BAVED SER [24] & 89 \\ Our Model SER with Saudi Dialect dataset & 96.81 \\ Our Model SER with BAVED dataset & 92.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing Our Model with State-of-the-Art Models. shifting, on classification performance. The results demonstrated a notable enhancement in accuracy, recall, precision, and F1-score for both datasets, with the Saudi Dialect dataset exhibiting a more pronounced benefit from augmentation due to its inherent complexity. For the Saudi Dialect dataset, the highest level of performance was achieved by combining two augmentation techniques, resulting in an accuracy of 96.81% and an F1-score of 96.76%. Similarly, the BAVED dataset demonstrated considerable improvement, with a combination of three augmentations yielding the best results, achieving an accuracy of 92.60% and an F1-score of 92.63%. These findings highlight the importance of data augmentation in addressing challenges specific to underrepresented dialects or imbalanced datasets. The comparative analysis further demonstrated that the selection and combination of augmentation techniques are crucial for improving SER performance. While individual augmentations provided substantial improvements, combining complementary techniques yielded the most favorable results, reinforcing the role of augmentation to enhance model generalization and robustness. Despite these promising results, a limitation of this study is the relatively small dataset size, which may affect the model's ability to generalize to entirely new and unexpected data. To mitigate this, cross-validation was used to assess the stability of the model across different dataset splits.