# 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 & 100.0 & 100.0 & 100.0 & 100.0 & 98.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 96.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 98.0 & 100.0 & 99.0 \\ \hline Accuracy & 100.0 & 100.0 & 100.0 & 98.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline \multicolumn{8}{c}{GBM} \\ \hline Precision & 93.0 & 100.0 & 100.0 & 94.0 & 100.0 & 95.0 & 99.0 & 93.0 \\ \hline Recall & 92.0 & 100.0 & 100.0 & 93.0 & 97.0 & 100.0 & 76.0 & 100.0 \\ \hline F1-score & 92.0 & 100.0 & 100.0 & 93.0 & 98.0 & 97.0 & 86.0 & 96.0 \\ \hline Accuracy & 95.0 & 91.0 & 92.0 & 88.0 & 91.0 & 90.0 & 96.0 & 98.0 \\ \hline \multicolumn{8}{c}{**ABC**} \\ \hline Precision & 92.0 & 100.0 & 96.0 & 94.0 & 100.0 & 95.0 & 93.0 & 97.0 \\ \hline Recall & 91.0 & 100.0 & 100.0 & 95.0 & 93.0 & 76.0 & 100.0 & 100.0 \\ \hline F1-score & 92.0 & 100.0 & 98.0 & 93.0 & 95.0 & 91.0 & 87.0 & 98.0 \\ \hline Accuracy & 93.0 & 90.0 & 92.0 & 86.0 & 93.0 & 91.0 & 95.0 & 96.0 \\ \hline \multicolumn{8}{c}{**BSVM**} \\ \hline Precision & 91.0 & 100.0 & 99.0 & 92.0 & 89.0 & 74.0 & 83.0 & 94.0 \\ \hline Recall & 91.0 & 100.0 & 100.0 & 90.0 & 86.0 & 85.0 & 86.0 & 98.0 \\ \hline F1-score & 93.0 & 100.0 & 100.0 & 91.0 & 86.0 & 77.0 & 87.0 & 95.0 \\ \hline Accuracy & 93.0 & 93.0 & 92.0 & 86.0 & 90.0 & 91.0 & 93.0 & 96.0 \\ \hline \multicolumn{8}{c}{**BMLP**} \\ \hline Precision & 92.0 & 100.0 & 93.0 & 90.0 & 88.0 & 73.0 & 81.0 & 91.0 \\ \hline Recall & 91.0 & 100.0 & 97.0 & 93.0 & 88.0 & 86.0 & 88.0 & 97.0 \\ \hline F1-score & 90.0 & 100.0 & 91.0 & 88.0 & 83.0 & 75.0 & 84.0 & 91.0 \\ \hline Accuracy & 92.0 & 91.0 & 93.0 & 89.0 & 91.0 & 88.0 & 92.0 & 94.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage precision, recall and F1-score and accuracy on RAVDESS HAF. Table 9 shows the analysis results of the percentage precision, recall, F1-score and accuracy obtained after performing the classification with SAVEE MFCC2 features. It can be observed from the table that all the learning algorithms performed well for recognizing the surprise emotion, but performed poorly in recognizing other emotions. In general, all learning algorithms achieved a low prediction of emotions with the exception of surprise emotion in which the perfect F1-score was obtained using the RDF algorithm. The lowest precision value of 93.0% was achieved for the surprise emotion using MFCC2 features with BMLP. These results indicate the unsuitability of MFCC, ZCR, energy and fundamental frequency features for recognizing human emotions. However, RDF and GBM gave higher recognition results for happy and sad emotions, RDF gave a higher recognition result for fear emotion while ABC and BSVM gave higher recognition results for the sad emotion that the traditional and bottleneck features [27].