# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 5 Conclusions  
The automatic recognition of emotion is still an open research because human emotions are highly influenced by quite a number of external factors. The primary contribution of this study is the construction and validation of a set of hybrid acoustic features based on prosodic and spectral \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Emotion**} \\ \hline
**Classifier/Measure** & **Angry** & **Disgust** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline \multicolumn{8}{c}{**RDF**} \\ \hline Precision & 100.0 & 100.0 & 94.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Recall & 100.0 & 94.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 97.0 & 97.0 \\ \hline Accuracy & 100.0 & 99.0 & 97.0 & 100.0 & 99.1 & 100.0 & 100.0 \\ \hline \multicolumn{8}{c}{**GBM**} \\ \hline Precision & 100.0 & 96.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Recall & 100.0 & 100.0 & 94.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 98.0 & 97.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Accuracy & 100.0 & 98.1 & 99.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline \multicolumn{8}{c}{**ABC**} \\ \hline Precision & 100.0 & 94.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Recall & 100.0 & 100.0 & 96.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 94.0 & 91.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Accuracy & 100.0 & 99.0 & 99.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline \multicolumn{8}{c}{**BSVM**} \\ \hline Precision & 100.0 & 93.0 & 99.0 & 99.0 & 100.0 & 100.0 & 100.0 \\ \hline Recall & 100.0 & 96.0 & 98.0 & 99.0 & 100.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 96.0 & 99.0 & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline Accuracy & 100.0 & 94.0 & 96.0 & 96.0 & 100.0 & 100.0 & 100.0 \\ \hline \multicolumn{8}{c}{**BMLP**} \\ \hline Precision & 100.0 & 90.0 & 98.0 & 97.0 & 100.0 & 100.0 & 100.0 \\ \hline Recall & 100.0 & 93.0 & 97.0 & 99.0 & 100.0 & 100.0 & 100.0 \\ \hline F1-score & 100.0 & 91.0 & 98.0 & 98.0 & 100.0 & 100.0 & 100.0 \\ \hline Accuracy & 100.0 & 93.0 & 95.0 & 99.0 & 100.0 & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Percentage precision, recall and F1-score and accuracy on SAVEE HAF. features for improving speech emotion recognition. The constructed acoustic features have made it easy to effectively recognize eight classes of human emotions as seen in this paper. The agglutination of prosodic and spectral features has been demonstrated in this study to yield excellent classification results. The proposed set of acoustic features is highly effective in recognizing all the eight emotions investigated in this study, including the fear emotion that has been reported in the literature to be difficult to classify. The proposed methodology seems to work well because combining certain prosodic and spectral features increases the overall discriminating power of the features hence increasing classification accuracy. This superiority is further underlined by the fact that the same ensemble learning algorithms were used on different feature sets, exposing the performance of each group of features regarding their discriminating power. In addition, we saw that ensemble learning algorithms generally performed well. They are widely judged to improve recognition performance better than a single inducer by decreasing variability and reducing bias. Experimental results show that it was quite difficult to achieve high precisions and accuracies when recognizing the neutral emotion with either pure MFCC features or a combination of MFCC, ZCR, energy and fundamental frequency features that were seen to be effective in recognizing the surprise emotion. However, we provided evidence through intensive experimentation that random decision forest ensemble learning of the proposed hybrid acoustic features was highly effective for speech emotion recognition. The results of this study were generally fantastic, even though the Gaussian noise was added to the training data.