## 4 Experimental Results
### Measuring Tools Used for the Evaluation  
Several metrics were used to measure the performance of the SER model on the test set across the five datasets in the evaluation, including accuracy, loss, precision, F1 score, and recall values; a confusion matrix; and receiver operating characteristic (ROC) curves [38].  
Each class [i] was evaluated by quantifying the true positives (TP_[i]), true negatives (TN_[i]), false positives (FP_[i]), and false negatives (FN_[i]). This process resulted in the determination of performance metrics.  
Accuracy is a metric used to determine the model's prediction accuracy by determining the ratio between accurate predictions and the sum of all predictions, using the following equation:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{FP}+\text{FN}+\text{TP}+\text{ TN}}\times 100. \tag{11}\]  
Precision is a metric that denotes the proportion of true positive predictions relative to all positive predictions. Determining this value involves calculating the proportion of true positives in relation to the combined total of true and false positives, using the following equation:  
\[\text{Precision}=\frac{\text{TP\_i}}{\text{TP\_i}+\text{FP\_i}}\times 100. \tag{12}\]  
The recall metric denotes the percentage of true positive predictions among all the positives. This is defined as the relationship between accurate positives and the sum of true positives and false negatives. This is calculated as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{13}\]  
The F1 score provides a means of balancing the precision and recall. It represents the weighted average of the precision and recall, which is determined using the following equation:  
\[\text{F1 score}=2*\frac{\text{precision}_{\texti}\times\text{Sensitivity}_{ \texti}}{\text{precision}_{\texti}+\text{Sensitivity}_{\texti}}\times 100. \tag{14}\]  
A confusion matrix indicates the class-prediction distribution of the classification model. To calculate the precision, recall, sensitivity, and specificity, a confusion matrix evaluates the TP, TN, FP, and FN metrics used to identify the model performance issues.  
The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds. The TPR and FPR are formally defined using mathematical expressions, with the TPR formula determining the ratio of true positives to the sum of true positives and false negatives by identifying the ratio of false positives to the sum of false positives and true negatives.  
\[\text{TPR}=\frac{\text{TruePositives}}{\text{TruePositives}+\text{FalseNegatives}}. \tag{15}\]  
\[\text{FPR}=\frac{\text{FalsePositives}}{\text{FalsePositives}+\text{True Negatives}}. \tag{16}\]  
The Matthews Correlation Coefficient (MCC) is a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes; it is defined as follows:  
\[\text{MCC}=\frac{(\text{TP}\times\text{TN})-(\text{FP}\times\text{FN})}{ \sqrt{(\text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text {TN}+\text{FN})}} \tag{17}\]