## 4 Results
### Validation of the Proposed Approach on the EmoUERJ Dataset  
The overall performance achieved using the designed audio features and five individual classifiers (1D-CNN, SVM, RF, GNB, and MLP) on the EmoUERJ dataset is presented in Table 4, along with the ensemble model DBMM. Table 5 details the performance of each classifier per emotion within the same dataset. Table 6 presents a comparative analysis of our approach and the study presented in [38] for the EmoUERJ dataset.  
* **Individual Classifier Performance**: The 1D-CNN model consistently outperformed other individual classifiers across most emotions. It achieved the highest accuracy for \"happy\" (89%) and \"sad\" (95%) emotions. However, its performance was slightly lower for \"neutral\" (79%) and \"angry\" (78%) emotions. The RF model demonstrated comparable performance to 1D-CNN for \"sad\" (91%) and \"angry\" (84%) emotions. However, it exhibited lower accuracy for \"happy\" (59%) and \"neutral\" (81%) emotions. GNB performed moderately well on \"happy\" (77%) and \"neutral\" (81%) emotions but showed lower accuracy for \"sad\" (71%) and \"angry\" (64%) emotions. The MLP model performed well across all emotions, with high accuracy for \"sad\" (95%) and \"angry\" (79%). Its performance for \"happy\" (75%) and \"neutral\" (87%) was competitive with other classifiers. Analyzing the overall results, the top three individual classifiers are 1D-CNN, MLP, and RF.
* **Ensemble Model Performance (DBMM)**: The DBMM ensemble model, combining the 1D-CNN, MLP, and RF classifiers, significantly outperformed all individual classifiers on the EmoUERJ dataset. It achieved the highest overall accuracy of 94%, with precision, recall, and F1-score all at 94%. This demonstrates the effectiveness of ensemble methods in leveraging the strengths of different classifiers to improve overall performance. The DBMM excelled in classifying the \"neutral\" emotion (99% precision, 96% recall, 98% F1-score), which was a challenge for some individual classifiers.
* **Cross-Linguistic Analysis**: The results in Table 6 compare the performance of our feature model + DBMM when trained on the EmoUERJ dataset and tested on both EmoUERJ and the ESD (English) dataset. Our model demonstrated high competitiveness with the study presented by [38] when tested on EmoUERJ. While our approach achieved high performance when trained on the ESD dataset and tested on the EmoUERJ dataset, the opposite scenario, training on EmoUERJ and testing on ESD, resulted in lower performance. This suggests that the model trained on the Portuguese EmoUERJ dataset might not generalize well to the English language dataset, likely due to differences in acoustic features and emotional expression patterns between languages.
* **Discussion**: The results on the EmoUERJ dataset underscore the effectiveness of ensemble-based methods like DBMM in improving speech emotion recognition performance compared to individual classifiers. Among the individual classifiers, 1D-CNN emerged as the strongest performer, closely followed by MLP. These findings suggest that deep learning models, when combined with appropriate feature engineering, can effectively capture and classify emotional nuances in speech. The cross-linguistic analysis emphasizes the need for further exploration of models and techniques that can generalize well across different languages and cultural contexts. Future work could explore methods for adapting models to different languages or creating more language-agnostic features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Overall PREC** & **Overall REC** & **Overall F1-Score** & **Overall ACC** \\ \hline
1D-CNN & 0.89 & 0.84 & 0.84 & 0.86 \\ \hline SVM & 0.75 & 0.76 & 0.74 & 0.73 \\ RF & 0.78 & 0.79 & 0.77 & 0.76 \\ GNB & 0.73 & 0.75 & 0.73 & 0.72 \\ \hline MLP & 0.79 & 0.81 & 0.79 & 0.77 \\ \hline
**DBMM** & **0.95** & **0.93** & **0.94** & **0.94** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance using multiple classifiers on EmoUERJ dataset.