# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Emotion**} \\ \hline
**Classifier/Measure** & **Angry** & **Calm** & **Disgust** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline \multicolumn{8}{c}{**RDF**} \\ \hline Precision & 100.0 & 100.0 & 100.0 & 92.0 & 80.0 & 53.0 & 87.0 & 96.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 83.0 & 82.0 & 86.0 & 79.0 & 91.0 \\ \hline F1-score & 100.0 & 100.0 & 100.0 & 87.0 & 81.0 & 66.0 & 83.0 & 94.0 \\ \hline Accuracy & 91.0 & 90.0 & 91.0 & 89.0 & 91.0 & 89.0 & 91.0 & 93.0 \\ \hline \multicolumn{8}{c}{**GBM**} \\ \hline Precision & 94.0 & 100.0 & 100.0 & 87.0 & 69.0 & 39.0 & 87.0 & 85.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 74.0 & 69.0 & 78.0 & 72.0 & 87.0 \\ \hline F1-score & 97.0 & 100.0 & 100.0 & 80.0 & 69.0 & 52.0 & 79.0 & 86.0 \\ \hline Accuracy & 86.0 & 85.2 & 87.0 & 81.0 & 83.0 & 85.0 & 86.0 & 90.0 \\ \hline \multicolumn{8}{c}{**ABC**} \\ \hline Precision & 91.0 & 82.0 & 78.0 & 79.0 & 81.0 & 79.0 & 81.0 & 81.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 81.0 & 56.0 & 85.0 & 71.0 & 82.0 \\ \hline F1-score & 95.0 & 100.0 & 91.0 & 65.0 & 69.0 & 70.0 & 81.0 & 79.0 \\ \hline Accuracy & 85.0 & 84.0 & 85.0 & 75.0 & 81.0 & 79.0 & 87.0 & 88.0 \\ \hline \multicolumn{8}{c}{**BSVM**} \\ \hline Precision & 83.0 & 84.0 & 82.0 & 75.0 & 75.0 & 77.0 & 79.0 & 89.0 \\ \hline Recall & 82.0 & 83.0 & 91.0 & 73.0 & 79.0 & 81.0 & 84.0 & 87.0 \\ \hline F1-score & 81.0 & 82.0 & 79.0 & 79.0 & 81.0 & 79.0 & 82.0 & 84.0 \\ \hline Accuracy & 83.0 & 85.0 & 81.0 & 79.0 & 80.6 & 81.0 & 86.0 & 86.0 \\ \hline \multicolumn{8}{c}{**BMLP**} \\ \hline Precision & 72.0 & 81.0 & 78.0 & 68.0 & 70.0 & 70.0 & 75.0 & 81.0 \\ \hline Recall & 78.0 & 84.0 & 82.0 & 70.0 & 76.0 & 79.0 & 78.0 & 83.0 \\ \hline F1-score & 74.0 & 82.0 & 80.0 & 67.0 & 71.0 & 75.0 & 77.0 & 80.0 \\ \hline Accuracy & 80.0 & 77.0 & 76.0 & 71.0 & 74.0 & 75.0 & 75.0 & 77.0 \\ \hline \end{tabular}
\end{table} 
Table 5: Percentage precision, recall and F1-score and accuracy on RAVDESS MFCC1. Emotion recognition results shown in Table 7 with respect to the RAVDESS HAF were generally impressive across emotions, irrespective of the ensemble learning algorithms utilized. RDF was highly effective in recognizing all the eight emotions because it had achieved perfect accuracy value by 100.0% on almost all the emotions, except fear (98.0%). Moreover, perfect precision and accuracy values were achieved in recognizing the neutral emotion, which was really difficult to identify using other sets of features. In addition, 98.0% accuracy and perfect values obtained for precision, recall and F1-score for the fear emotion is impressive because of the difficulty of recognizing the fear emotion as reported in the literature [25; 26; 27]. All the other learning algorithms performed relatively well, considering the fact that noise was added to the training data. These results attest to the effectiveness of the proposed HAF for recognizing emotions in human speech. In addition, the results point to the significance of sourcing highly discriminating features for recognizing human emotions. They further show that the fear emotion could be recognized efficiently when compared to other techniques used in [44]. It can be seen that the recognition of fear emotion was successfully improved using the proposed HAF. In addition, HAF gave better performance than the bottleneck features used for six emotions from CASIA database [27] that achieved a recognition rate of 60.5% for the fear emotion.