## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1\_Score (\%)** \\ \hline \multicolumn{5}{c}{Without augmentation} \\ \hline Original Dataset (OD) & 42.85 & 42.85 & 46.79 & 43.49 \\ \hline \multicolumn{5}{c}{With one augmentation} \\ \hline OD + noise(0.05) & 74.60 & 74.60 & 77.76 & 72.46 \\ OD + volumeUp(2) & 88.89 & 88.89 & 89.83 & 88.61 \\ OD + volumeDown(0.5) & 87.30 & 87.30 & 87.81 & 86.57 \\ OD + timeShift(0.1) & 68.25 & 68.25 & 75.09 & 66.32 \\ \hline \multicolumn{5}{c}{With two augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) & 89.36 & 89.36 & 90.14 & 89.41 \\ OD + noise(0.05) + volumeDown(0.5) & 90.43 & 90.43 & 91.05 & 90.33 \\ OD + noise(0.05) + timeShift(0.1) & 84.04 & 84.04 & 86.33 & 84.51 \\ OD + volumeUp(2) + volumeDown(0.5) & 96.81 & 96.81 & 97.01 & 96.76 \\ OD + volumeUp(2) + timeShift(0.1) & 89.36 & 89.36 & 90.02 & 89.18 \\ OD + volumeDown(0.8) + timeShift(0.1) & 88.30 & 88.30 & 89.68 & 88.00 \\ \hline \multicolumn{5}{c}{With three augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) + volumeDown(0.5) & 95.24 & 95.24 & 95.77 & 95.26 \\ OD + noise(0.05) + volumeDown(0.5) + timeShift(0.1) & 85.71 & 85.71 & 86.59 & 85.86 \\ OD + noise(0.02) + volumeUp(2) + timeShift(0.1) & 92.86 & 92.86 & 93.47 & 92.72 \\ OD + volumeUp(2) + volumeDown(0.5) + timeShift(0.1) & 95.20 & 95.20 & 95.36 & 95.16 \\ \hline \multicolumn{5}{c}{With four augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) + volumeDown(0.5) & 96.13 & 96.13 & 96.26 & 96.11 \\ + timeShift(0.1) & \multicolumn{1}{c}{} & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Classification report using various data augmentation techniques and different numbers of augmentations on the Saudi Dialect dataset (OD = Original Dataset). The highest scores are in bold. \begin{table}
\begin{tabular}{c c c c c} \hline
**Data** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1\_Score (\%)** \\ \hline \multicolumn{5}{c}{Without augmentation} \\ \hline Original Dataset (OD) & 65.89 & 65.89 & 66.36 & 65.85 \\ \hline \multicolumn{5}{c}{With one augmentation} \\ \hline OD + noise(0.05) & 72.28 & 72.28 & 72.71 & 72.35 \\ OD + volumeUp(2) & 75.32 & 75.32 & 78.79 & 75.30 \\ OD + volumeDown(0.5) & 78.14 & 78.14 & 78.18 & 78.08 \\ OD + timeShift(0.1) & 76.44 & 76.44 & 78.67 & 76.15 \\ \hline \multicolumn{5}{c}{With two augmentations} \\ \hline OD + noise(0.05) + volumeUp(2) & 82.31 & 82.31 & 82.69 & 82.22 \\ OD + noise(0.05) + volumeDown(0.5) & 86.54 & 86.54 & 86.80 & 86.59 \\ OD + noise(0.05) + timeShift(0.1) & 80.86 & 80.86 & 81.62 & 80.96 \\ OD + volumeUp(2) + volumeDown(0.5) & 88.45 & 88.45 & 89.00 & 88.47 \\ OD + volumeUp(2) + timeShift(0.1) & 83.72 & 83.72 & 84.03 & 83.79 \\ OD + volumeDown(0.8) + timeShift(0.1) & 83.79 & 83.79 & 84.23 & 83.88 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification report using various data augmentation techniques and different numbers of augmentations on the BAVED dataset (OD = Original Dataset). The highest scores are in bold. ### Saudi Dialect Dataset

For the Saudi Dialect dataset, the baseline model without augmentation achieved an accuracy of 42.85%, highlighting the challenges associated with this dataset. The application of individual augmentations significantly improved performance, with volume adjustments yielding the best results. Specifically, decreasing the volume (OD + volumeUp) achieved the highest accuracy of 88.89% and an F1-score of 88.61%. When two augmentation techniques were combined, the best results were achieved with the combination of volume increase and volume decrease (OD + volumeUp + volumeDown), resulting in an accuracy of 96.81% and an F1-score of 96.76%. These findings align with the type of data augmentation techniques (one augmentation), where augmentations involving volume increase and volume reduction outperformed other types of data augmentation.