## 4 Results
### Validation of the Proposed Approach on the Emotional Speech Dataset (ESD)  
We evaluated the performance of five individual classifiers (1D-CNN, SVM, RF, GNB, and MLP) and the DBMM ensemble model on the ESD dataset, which includes five emotion classes (happy, neutral, sad, angry, and surprise) recorded in English by 10 native speakers [37]. Table 7 presents the overall accuracy attained on the ESD dataset, while Table 8 details per-class performance metrics (precision, recall, F1-score) for each classifier. Table 9 presents a comparative analysis of our approach and the study presented in [38]. * **Individual Classifier Performance**: The 1D-CNN exhibited robust performance across all emotion classes, achieving high precision, recall, and F1-scores, with an overall accuracy of 87%. It was particularly competitive in recognizing happiness (F1-score: 82%) and sadness (F1-score: 92%). The SVM demonstrated balanced performance across classes. RF showed competitive results, excelling in classifying neutral emotions \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Language** & **Tested Language** & **ACC** \\ \hline \multirow{2}{*}{Wav2 Vec2-XLSR [25]} & Portuguese & Portuguese & \\  & (EmoUER) & (EmoUER) & 0.92 \\ \hline \multirow{2}{*}{Wav2 Vec2-XLSR [25]} & \multirow{2}{*}{English (ESD)} & Portuguese & \\  & & & (EmoUER) & \\ \hline \multirow{2}{*}{**Our Approach**} & Portuguese & Portuguese & \\  & (EmoUER) & (EmoUER) & **0.94** \\ \hline \multirow{2}{*}{**Our Approach**} & \multirow{2}{*}{English (ESD)} & Portuguese & \\  & & & (EmoUER) & **0.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of our approach with the study referenced in [38]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**(a) 1D CNN** & \multicolumn{8}{c}{**(b) SVM**} \\ \hline  & **PREC** & **REC** & **F1** & **Support** & & **PREC** & **REC** & **F1** & **Support** \\ \hline \multirow{2}{*}{Happy} & 0.89 & 0.7 & 0.78 & 26 & Happy & 0.79 & 0.59 & 0.67 & 26 \\ \hline \multirow{2}{*}{Neutral} & 0.95 & 0.79 & 0.86 & 17 & Neutral & 0.72 & 0.64 & 0.67 & 17 \\ \hline \multirow{2}{*}{Sad} & 0.95 & 0.96 & 0.95 & 14 & Sad & 0.85 & 0.91 & 0.88 & 14 \\ \hline \multirow{2}{*}{Angry} & 0.78 & 0.92 & 0.85 & 19 & Angry & 0.64 & 0.89 & 0.75 & 19 \\ \hline \multirow{2}{*}{**ACC**} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \\ \hline Macro avg & 0.89 & 0.84 & 0.86 & 76 & Macro avg & 0.75 & 0.76 & 0.73 & 76 \\ \hline \multirow{2}{*}{Weighted avg} & 0.89 & 0.84 & 0.84 & 76 & Weight avg & 0.75 & 0.73 & 0.73 & 76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results per emotion on EmoUERJ dataset. (F1-score: 86%) but struggling with happiness. GNB exhibited the lowest overall performance. MLP emerged as the top-performing individual classifier, with an overall accuracy of 92%. * **Ensemble Model Performance (DBMM)**: The DBMM ensemble model, combining 1D-CNN, MLP, and RF, outperformed all individual classifiers, achieving the highest overall accuracy of 97%. It also demonstrated superior performance across all emotion classes, with F1-scores consistently above 97%. * **Cross-Linguistic Analysis**: In Table 9, we compare our feature model + DBMM performance when trained on the ESD dataset and tested on both the ESD dataset (following the 80/20 split protocol [38]) and the EmoUERJ dataset [36]. Interestingly, the model trained on the English ESD dataset performed well on the Portuguese EmoUERJ dataset, while the reverse was not as successful. This suggests that the English dataset, with its greater diversity in sentences, speakers, and samples, offers a more generalizable representation of emotional expression. * **Discussion**: These results underscore the effectiveness of ensemble methods like DBMM in leveraging the strengths of multiple classifiers to achieve superior performance in SER.