## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{5}{c}{**Without GWO Optimizer**} & \multicolumn{5}{c}{**GWO Optimizer**} \\  & **Measurements** & **SVM** & **K-SVM** & **CNN** & **LSTM** & **SVM** & **K-SVM** & **CNN** & **LSTM** \\ \hline \multirow{5}{*}{EmoDB} & Accuracy & 0.74 & 0.55 & 0.75 & 0.76 & 0.85 & 0.85 & 0.78 & 0.83 \\  & Precision & 0.71 & 0.42 & 0.74 & 0.73 & 0.82 & 0.87 & 0.77 & 0.82 \\  & Recall & 0.70 & 0.45 & 0.69 & 0.71 & 0.82 & 0.84 & 0.77 & 0.87 \\  & F1-Score & 0.70 & 0.39 & 0.71 & 0.71 & 0.82 & 0.84 & 0.77 & 0.84 \\  & Accuracy & 0.74 & 0.55 & 0.53 & 0.73 & 0.85 & 0.87 & 0.60 & 0.73 \\  & Precision & 0.71 & 0.42 & 0.53 & 0.73 & 0.84 & 0.88 & 0.59 & 0.69 \\  & Recall & 0.70 & 0.45 & 0.52 & 0.82 & 0.83 & 0.85 & 0.56 & 0.71 \\  & F1-Score & 0.70 & 0.39 & 0.52 & 0.76 & 0.83 & 0.86 & 0.59 & 0.71 \\  & Accuracy & 0.54 &.35 & 0.57 & 0.62 & 0.76 & 0.75 & 0.68 & 0.71 \\  & Precision & 0.53 & 0.10 & 0.59 & 0.53 & 0.74 & 0.72 & 0.66 & 0.59 \\  & Recall & 0.51 & 0.25 & 0.51 & 0.57 & 0.75 & 0.68 & 0.64 & 0.72 \\  & F1-Score & 0.52 & 0.15 & 0.53 & 0.59 & 0.73 & 0.68 & 0.64 & 0.71 \\  & Accuracy & 0.98 & 0.91 & 0.99 & 0.97 & 0.99 & 0.99 & 0.99 & 0.99 \\  & Precision & 0.98 & 0.93 & 0.99 & 0.96 & 0.99 & 0.99 & 0.99 & 0.99 \\  & Recall & 0.98 & 0.91 & 0.99 & 0.95 & 0.99 & 0.99 & 0.99 & 0.99 \\  & F1-Score & 0.98 & 0.91 & 0.99 & 0.96 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification Performance of 6 classifiers on the four datasets. Figure 4: _Cont_. Figure 4: The confusion matrix of GWO-CNN and CNN model was tested on four datasets—RAVDESS, SAVEE, TESS, and EMODB. Figure 4g,h display the confusion matrix, representing a set of emotions comprising six categories: anger, happiness, neutral, fear, disgust, and sadness for TESS. The proposed model achieved an overall accuracy of 99% when evaluated using this particular emotion set and an accuracy of 100% when evaluated with the Gray Wolf Optimization technique. The TESS data are highly recommendable for speech emotion recognition as all the emotions are accurately classified by our model as well. The TESS dataset does not need Gray Wolf Optimization since it is already good enough to be used for accurate emotion classification via traditional methods. In Figure 5a, the horizontal axis represents the number of epochs, and the vertical axis represents the accuracy and the validation accuracy through training. The accuracy peaks at nearly 90% once, while the validation accuracy rises with it to around 70% and then flattens. The model was set to train for 100 epochs with an Early Stopping Callback. This stopped the training at 61 epochs, denoting that training the model will no longer improve the results. As visible in Figure 5b, the horizontal axis represents the number of epochs, and the vertical axis represents the loss and the validation loss through training. The loss is relatively high at the beginning of training but decreases gradually. After a while, the validation loss starts to saturate and does not fall below the 0.11 mark. As visible in Figure 5c, the accuracy is relatively low at the beginning of training, but it increases gradually. The accuracy peaks at nearly 99% once, while the validation accuracy rises slower than that. The model was set to train for 100 epochs with an Early Stopping Callback. This stopped the training at 67 epochs, denoting that training the model will no longer improve the results. The testing accuracy is only 53%. As visible in Figure 5d, the x-axis represents the number of epochs, and the y-axis represents the loss and the validation loss through training. It is evident that the validation loss does not decrease and instead fluctuates between 0.14 and 0.12 at all times. This is why the testing accuracy and the validation accuracy are very low. In Figure 5e, the horizontal axis represents the number of epochs, and the vertical axis represents the accuracy and the validation accuracy through training.