# 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} \hline \hline  & \multicolumn{6}{c}{**Emotion**} \\ \hline
**Classifier/Measure** & **Angry** & **Disgust** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline \multicolumn{6}{c}{**RDF**} \\ \hline Precision & 65.0 & 24.0 & 65.0 & 48.0 & 94.0 & 48.0 & 100.0 \\ \hline Recall & 65.0 & 83.0 & 57.0 & 65.0 & 52.0 & 83.0 & 100.0 \\ \hline F1-score & 65.0 & 37.0 & 60.0 & 55.0 & 67.0 & 61.0 & 100.0 \\ \hline Accuracy & 69.0 & 66.0 & 60.0 & 75.0 & 64.0 & 77.0 & 81.0 \\ \hline \multicolumn{6}{c}{**GBM**} \\ \hline Precision & 65.0 & 29.0 & 75.0 & 26.0 & 91.0 & 62.0 & 100.0 \\ \hline Recall & 62.0 & 67.0 & 48.0 & 55.0 & 62.0 & 72.0 & 100.0 \\ \hline F1-score & 63.0 & 40.0 & 59.0 & 35.0 & 74.0 & 67.0 & 100.0 \\ \hline Accuracy & 70.0 & 66.0 & 57.0 & 74.0 & 62.0 & 79.0 & 84.0 \\ \hline \multicolumn{6}{c}{**ABC**} \\ \hline Precision & 66.0 & 28.0 & 64.0 & 36.0 & 89.0 & 59.0 & 98.0 \\ \hline Recall & 68.0 & 29.0 & 62.0 & 40.0 & 91.0 & 59.0 & 100.0 \\ \hline F1-score & 65.0 & 28.0 & 63.0 & 38.0 & 91.0 & 58.0 & 99.0 \\ \hline Accuracy & 60.0 & 57.0 & 53.0 & 66.0 & 62.0 & 79.0 & 83.0 \\ \hline \multicolumn{6}{c}{**BSVM**} \\ \hline Precision & 65.0 & 23.0 & 61.0 & 41.0 & 85.0 & 57.0 & 98.0 \\ \hline Recall & 64.0 & 26.0 & 57.0 & 38.0 & 81.0 & 61.0 & 100.0 \\ \hline F1-score & 66.0 & 29.0 & 62.0 & 36.0 & 83.0 & 58.0 & 99.0 \\ \hline Accuracy & 56.0 & 54.0 & 51.0 & 69.0 & 56.0 & 77.0 & 85.0 \\ \hline \multicolumn{6}{c}{**BMLP**} \\ \hline Precision & 64.0 & 23.0 & 59.0 & 38.0 & 83.0 & 54.0 & 95.0 \\ \hline Recall & 63.0 & 25.0 & 55.0 & 39.0 & 85.0 & 57.0 & 97.0 \\ \hline F1-score & 64.0 & 25.0 & 59.0 & 37.0 & 81.0 & 55.0 & 96.0 \\ \hline Accuracy & 52.0 & 50.0 & 49.0 & 51.0 & 53.0 & 64.0 & 69.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Percentage precision, recall and F1-score and accuracy on SAVEE MFCC1. Table 10 shows the analysis results of the percentage precision, recall, F1-score and accuracy obtained after performing classification with SAVEE HAF. Accordingly, all the emotions were much easier to recognize using the HAF that had tremendously improved the recognition of the fear emotion reported in the literature to be difficult with lower performance results recorded with traditional and bottleneck features for fear emotion [27]. Moreover, [PERSON] et al. [11] obtained a recognition accuracy rate of 76.16% for the fear emotion using MFCC and MS features based on the Spanish database of seven emotions. These results show that using HAF with ensemble learning is highly promising for recognizing emotions in human speech. The study findings were consistent with the literature that ensemble learning gives a better predictive performance through the fusion of information knowledge of predictions of multiple inducers [75; 85]. The ability of ensemble learning algorithms to mimic the nature of human by seeking opinions from several inducers for informed decision distinguish them from inducers [73]. Moreover, it can be seen across Tables 4-10 that the set of HAF presented the most effective acoustic features for speech emotion recognition, while the set MFCC1 presented the worst features. In addition, the study results supported the hypothesis that a combination of acoustic features based on prosodic and spectral reflects the important characteristics of speech [42; 53; 54].