## 5 Experiments
### Classification Results  
We performed experiments for different models. Each experiment was repeated three times, and average accuracy with variance is reported as shown in Table 2. In Table 2, Support Vector Machine [31] and Multilayer perceptron [13] are multi class classifiers, CNN is our proposed architecture, and other models are the flavors of efficient Net that range from B0 to B7. Accuracy, defined as below, was used for evaluating the performance:  
\[A=\frac{C}{T} \tag{3}\]  
where \(A\) is accuracy, \(C\) is the number of samples recognized correctly, and \(T\) is the number of all samples. Furthermore, we also showed the accuracy at each epoch for all models, as in Figure 7. Among them, our proposed CNN model showed better performance. Additionally, our proposed CNN showed faster convergence, as in Figure 7. For evaluating the effectiveness of our proposed CNN on different languages, we performed the experiments on Gujarati [29] and English [41,42] spoken digits. Our proposed CNN also outperformed the Gujarati Digit Model by absolute 22% accuracy. We compared the proposed model's accuracy on the English digit dataset with multiple baseline models; the proposed CNN outperformed CNNDigit Reco [43], SVM [44], random forest [44], and English model results by absolute 19.3%, 7.3%, and 0.3%, respectively. Gujarati digit model and English digit model performances are compared in Table 3 and in Table 4. Furthermore, we checked \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Dimensions** & **Comments** \\ \hline input & input layer & \((32,32,1)\) \\ \hline CNN & \((30,30,64)\) & kernel 3 \(\times\) 3; stride 1; relu activation \\ \hline Max Pool & \((15,15,64)\) & N.A \\ \hline BN & \((15,15,64)\) & default value as given in Keras [39] \\ \hline CNN & \((13,13,64)\) & kernel 3 \(\times\) 3; stride 1; relu activation \\ \hline Max Pool & \((6,6,64)\) & N.A \\ \hline BN & \((6,6,64)\) & default value as given in Keras [39] \\ \hline CNN & \((4,4,64)\) & kernel 3 \(\times\) 3; stride 1; relu activation \\ \hline Max Pool & \((2,2,64)\) & N.A \\ \hline BN & \((2,2,64)\) & default value as given in Keras [39] \\ \hline Dropout & \((2,2,64)\) & dropout rate = 0.1 \\ \hline Flatten & 256 & N.A \\ \hline Fully connected & 512 & N.A \\ \hline Dropout & 512 & dropout rate = 0.1 \\ \hline Fully connected & 128 & N.A \\ \hline Dropout & 512 & dropout rate = 0.1 \\ \hline fully connected & 10 & softmax activation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CNN Model. the effectiveness of the proposed CNN on the Urdu Corpus [11] dataset. It is found that the proposed CNN outperformed LDA, SVM, and RF by absolute accuracy margins of 34.53%, 24.53%, and 34.53%, respectively, as it is shown in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Accuracy** \\ \hline CNNDigitReco-speakerindependent [43] & 0.78 \\ \hline Support Vector Machines [44] & 0.90 \\ \hline Random Forest [44] & 0.96 \\ \hline English Digit Model [41] & 0.97 \\ \hline Our CNN & 0.973 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of CNN on English Digits [41]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Accuracy** \\ \hline Support Vector Machine & 0.65 \(\pm\) 0.0 \\ \hline Multilayer Perceptron & 0.73 \(\pm\) 0.02 \\ \hline CNN & 0.86 \(\pm\) 0.02 \\ \hline EfficientNetB0 & 0.84 \(\pm\) 0.05 \\ \hline EfficientNetB1 & 0.82 \(\pm\) 0.02 \\ \hline EfficientNetB2 & 0.83 \(\pm\) 0.04 \\ \hline EfficientNetB3 & 0.84 \(\pm\) 0.06 \\ \hline EfficientNetB4 & 0.82 \(\pm\) 0.03 \\ \hline EfficientNetB5 & 0.84 \(\pm\) 0.04 \\ \hline EfficientNetB6 & 0.81 \(\pm\) 0.06 \\ \hline EfficientNetB7 & 0.56 \(\pm\) 0.07 \\ \hline \end{tabular}
\end{table} 
Table 2: Baseline accuracies of different models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Accuracy** \\ \hline Gujarati Digits Model [29] & 0.75 \\ \hline Our CNN & 0.97 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of CNN on Gujarati Digits [29].