## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume
Adding a third augmentation technique, such as noise, slightly reduced performance compared to the best two-augmentation setup, but it still resulted in high scores. The application of all four augmentations (OD + noise + volumeUp + volumeDown + timeShift) also led to a slight reduction in performance. A deeper analysis of Table 3 reveals that augmentations targeting volume adjustments consistently outperformed other techniques, whether applied individually or in combination. This indicates that volume-based augmentations address a significant source of variability in the Saudi Dialect dataset, possibly related to differences in recording conditions or speaker intensity. Furthermore, the results demonstrate the importance of balancing augmentation complexity. While combining two augmentations significantly enhanced performance, introducing additional augmentations sometimes introduced irrelevant variations, which slightly hindered the model's ability to learn meaningful patterns. ### BAVED Dataset

The BAVED dataset also showed notable improvements with data augmentation, though the baseline accuracy of 65.89% was higher compared to the Saudi Dialect dataset. Among individual augmentations, reducing the volume (OD + volumeDown) achieved the best performance, with an accuracy of 78.14% and an F1-score of 78.08%. Combining two augmentations, specifically volume increase and volume decrease (OD + volumeUp + volumeDown), resulted in the highest accuracy of 88.45% and an F1-score of 88.47%. Adding a third augmentation, such as noise, yielded the highest overall performance, with an accuracy of 92.60% and an F1-score of 92.63%. However, incorporating all four augmentation techniques resulted in similar performance (accuracy: 89.40%, F1-score: 89.20%), indicating diminishing returns with the addition of more augmentations. An in-depth analysis of Table 4 reveals that the BAVED dataset consistently benefited from volume-based augmentations, similar to the Saudi Dialect dataset. However, the most effective combination involved three augmentations (noise addition, volume increase, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1\_Score (\%)** \\ \hline \multicolumn{5}{c}{With three augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) + volumeDown(0.5) & 92.60 & 92.60 & 92.73 & 92.63 \\ OD + noise(0.05) + volumeDown(0.5) + timeShift(0.1) & 88.16 & 88.16 & 88.18 & 88.13 \\ OD + noise(0.02) + volumeUp(2) + timeShift(0.1) & 89.39 & 89.39 & 89.41 & 89.39 \\ OD + volumeUp(2) + volumeDown(0.5) + timeShift(0.1) & 89.14 & 89.14 & 89.13 & 89.07 \\ \hline \multicolumn{5}{c}{With four augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) + volumeDown(0.5) & 89.4 & 89.4 & 89.6 & 89.2 \\ + timeShift(0.1) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._volume decrease), rather than just two. This suggests that noise augmentation contributes more positively to BAVED due to its dataset characteristics, possibly because the recorded speech samples already contain some variations in volume, making additional adjustments more impactful when combined with noise. These findings highlight the importance of selective augmentation strategies tailored to the dataset's nature. While two augmentations were optimal for the Saudi Dialect dataset, a combination of three augmentations was most effective for BAVED, emphasizing the need for dataset-specific augmentation tuning to achieve the best results. A comparison of the results across the two datasets reveals that the Saudi Dialect dataset exhibited a more substantial enhancement following data augmentation, with an improvement of over 54 percentage points in accuracy from the baseline to the best-performing model. Conversely, the BAVED dataset demonstrated a more modest enhancement of 26 percentage points, likely attributable to its superior initial quality and equilibrium.