## 4 Experiments  
The four previously described datasets were used to run speaker-independent experiments to evaluate the proposed SER. It applied a percentage-split technique where it split the data into two sets, the first set including 80% of the data used to train the SER models, and the remaining 20% data were used to perform testing. Prior studies concluded that it is better to train the model using 70-80% of the dataset, and the remaining 30-20% should be used for testing purposes. This dataset splitting technique can help achieve improved results, and SER models prevent overestimating and overlapping results. Figure 2 presents the performance of the proposed transformer model applied to the datasets as mentioned above, using a feature set of 273 to train the model. The table provides information about the accuracy of the proposed model applied to selected datasets. Figure 2: _Cont._  
The highest predicted accuracy is shown in the confusion matrix (Figure 3a) of the BAVED dataset. It achieved 96% for anger/sad/happy emotion, 94% for tired/exhausted emotion, and 93% for neutral. The overall predicted accuracy of the proposed model also outperformed the existing SER models applied on BAVED [43]. The proposed model achieved 93% accuracy for EMO-DB and outperforms the baseline EMO-DB models. The proposed model predicts better accuracy because of discriminative features for all types of emotions. Figure 3b describes the accuracy and confusion of inter-emotions for the SER model. The diagonal values show the predicted values in the confusion matrix, while the values shown in equivalent rows represent the inter-emotion confusion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotion** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Anger & 79 & 97 & 79 & 87 \\ \hline Disgust & 81 & 89 & 81 & 85 \\ \hline Fear & 79 & 78 & 79 & 78 \\ \hline Happy & 77 & 70 & 77 & 73 \\ \hline Neutral & 93 & 90 & 93 & 91 \\ \hline Sad & 96 & 87 & 96 & 91 \\ \hline Surprise & 83 & 85 & 83 & 84 \\ \hline Average & 84 & 85 & 84 & 84 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the proposed model for the SAVEE dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Anger & 95 & 93 & 95 & 94 \\ \hline Boredom & 94 & 94 & 92 & 93 \\ \hline Disgust & 100 & 92 & 100 & 96 \\ \hline Fear & 94 & 87 & 94 & 90 \\ \hline Happy & 78 & 91 & 78 & 84 \\ \hline Neutral & 98 & 94 & 98 & 96 \\ \hline Sad & 97 & 100 & 97 & 99 \\ \hline Average & 93 & 93 & 93 & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the proposed model for the EMO-DB dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Anger & 97 & 90 & 97 & 94 \\ \hline Disgust & 83 & 79 & 83 & 81 \\ \hline Fear & 96 & 100 & 96 & 98 \\ \hline Joy & 93 & 97 & 93 & 95 \\ \hline Neutral & 87 & 100 & 87 & 93 \\ \hline Sad & 85 & 84 & 85 & 84 \\ \hline Surprise & 99 & 92 & 99 & 95 \\ \hline Average & 91 & 92 & 91 & 91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the proposed model for the EMOVO dataset. Figure 3c shows the SAVEE dataset confusion matrix that represents accuracies for all emotions. It presents 96% accuracy for sad emotion, 93% for neutral, 83% for surprise, 81% for disgust, and 79% for anger emotion. However, the fear emotion achieved the lowest accuracy. The possible reason behind this misclassification might be because the frequency of signals of this emotion is very similar to other emotions and hence is challenging for the proposed model to recognize emotion accurately. In the end, Figure 3d shows the confusion matrix of the EMOVO dataset. The average accuracy of the EMOVO SER model in emotion prediction is 91%.