# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Discussion  
The classification model's performance with data augmentation outperformed the model's performance without data augmentation. We can increase the performance of the network by reducing overfitting and improving the accuracy of the network as can be seen in Figure 9. In this figure, the left bar represents DCNN, the middle one represents AlexNet, and the right one represents the BLSTM network tested on two validation techniques including 5-fold CV, and the hold-out validation for the dataset with data augmentation and without data augmentation. Figure 9a shows alphabet classification results with and without data augmentation and Figure 9b shows pronunciation classification results with and without data augmentation. The results in Figure 9b are so close because it has only two classes, by increasing the number of classes we might see the difference very clearly.  
We can also see from the results given in previous sections that the AlexNet with transfer learning outperformed other networks, although its architecture is similar but deeper than the DCNN network. As a result that AlexNet is trained with 60 million parameters, the spectrograms are augmented by mirroring and cropping the images which increases variation in the training dataset. It uses overlapped pooling layers after some convolution layers which improves the error rate as compared to other networks. DCNN is the second network in the lead, we construct a small DCNN network as an array of layers seen in Figure 6 and then trained it from scratch. Due to over-fitting while training, its error rate is more than the AlexNet network. Now, the third network (BLSTM) has accuracy less than the other networks because it cannot extract features on its own while  
\begin{table}
\begin{tabular}{l l l|c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{3-4}  & **Class** & **Correct Pronunciation** & **Mispronunciation** \\ \hline DCNN & Correct pronunciation & 113 & 3 \\  & Mispronunciation & 3 & 113 \\ AlexNet & Actual & Correct pronunciation & 116 & 0 \\  & Mispronunciation & 3 & 113 \\ BLSTM & Correct pronunciation & 99 & 17 \\  & Mispronunciation & 47 & 69 \\ \hline \hline \end{tabular}
\end{table}
Table 10: DCNN without data augmentation.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{3-4}  & **Class** & **Correct Pronunciation** & **Mispronunciation** \\ \hline DCNN & Correct pronunciation & 788 & 24 \\  & Mispronunciation & 11 & 801 \\ AlexNet & Actual & Correct pronunciation & 806 & 6 \\  & Mispronunciation & 8 & 804 \\ BLSTM & Correct pronunciation & 781 & 31 \\  & Mispronunciation & 331 & 481 \\ \hline \hline \end{tabular}
\end{table}
Table 11: DCNN without data augmentation.  
both DCNN and AlexNet network extracts numerous features on their own from the spectrograms. Whereas, while using BLSTM we first extract the fixed number of features and then train it by using BLSTM, which is why its results are less satisfying than the other networks. The comparison of the ANNs with and without data augmentation for alphabet classification and alphabet pronunciation classification can be seen in Figure 9.