# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Arabic Alphabet Classification
#### 4.3.2 With Data Augmentation  
In this section, we are going to discuss the results of the DCNN, AlexNet, and BLSTM network trained over the whole dataset including source data and augmentation data in detail.  
DCNN: The columns and rows represent the predicted class and actual class. The diagonal cells show the number of correctly classified observations and the off-diagonal cells show the number of incorrect observations. The alphabet 'alif', 'ba', 'zhal', 'za','seen','sheen','saud', 'aain', 'gbian', 'kaaf','meem', and 'hamzah' have 100% accuracy. Only the observations of the misclassified alphabet are shown in Table 6. The overall accuracy of the DCNN is 95.95%.  
In cross validation, we have applied a 5-fold validation experiment on the DCNN model. We cross-check and validate the non-overlapping data k-fold times. The alphabet 'alif', 'zua', 'qauf', 'wao', and 'hamzah' have 100% accuracy. The accuracy of 93.46% by splitting the train and test datasets into 80% and 20%.  
Pre-trained AlexNet: By using pre-trained AlexNet model, the results achieved are better than the DCNN network. We achieved an accuracy of 98.41% with random split. We can see from Table 7 that the network is confusing two sample 'fa' with'sa'. One sample of 'duad' with 'zua', 'za' with 'zua', 'tau' with 'zua', 'qauf' with 'kaaf', 'ha' with 'hha' and'meem' with 'jeem' and vice versa. Accurately classified classes are 'alif', 'ba','sa', 'jeem', 'ra','seen','sheen','saud', 'aain', 'gbian', 'laam', 'noon', 'wao', 'ya', 'hamzah' having 100% accuracy. Whereas the network provides an accuracy of 96.72% using hold-out validation.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline
**seq.** & **sa** & **jeem** & **hha** & **kha** & **dal** & **zhal** & **za** & **seen** & **sheen** & **tua** & **zau** & **ain** & **qauf** & **kaaf** & **laam** & **meem** & **wao** & **ya** & **hamzah** & **Acc** \\ \hline sa & 28.0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 96.3\% \\ jeem & 0 & 33.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 96.7\% \\ hha & 0 & 0 & 22.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 90.8\% \\ kha & 0 & 0 & 0 & 29.1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 96.6\% \\ dal & 0 & 0 & 0 & 0 & 29.1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 96.7\% \\ zhal & 1 & 0 & 0 & 0 & 0 & 33.1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 96.7\% \\ za & 0 & 0 & 0 & 0 & 0 & 0 & 28.0 & 0 & 2 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 86.7\% \\ duad & 0 & 0 & 0 & 0 & 0 & 0 & 33.2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 96.8\% \\ tua & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 23.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 90.8\% \\ zua & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 By using 5-fold CV experiment using AlexNet, the model accurately classified the alphabet 'alif', 'ba', 'jeem', 'za','seen','sheen','suad', 'zua', 'gauf', 'tau', 'noon', 'wao', 'ya' and 'hamzah' have 100% accuracy. Overall accuracy of the network with 5-fold CV is 96.36%, respectively.  
BLSTM: In the BLSTM network, we achieved an accuracy of 87.90% with a random split and 88.38% with the hold-out validation. BLSTM is confusing some alphabet of matching sounds, i.e., 'ba', 'ta','sa', 'dal', 'zhal', 'tau', 'zua', 'ghani', 'fa', etc. The alphabet 'kha' and'seen' are accurately classified with 100% accuracy.  
By using a 5-fold CV experiment using the BLSTM network, we have achieved an accuracy of 87.95%. 'Kha' is the only alphabet that is accurately classified with 100% accuracy.