# 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} \hline \hline  & \multicolumn{3}{c}{**SAVEE Feature**} & \multicolumn{3}{c}{**RAVDESS Feature**} \\ \hline
**Classifier/Measure** & **MFCC1** & **MFCC2** & **HAF** & **MFCC1** & **MFCC2** & **HAF** \\ \hline \multicolumn{5}{c}{**RDF**} \\ \hline Precision & 63.4 (\(\pm\)0.044) & 78.1 (\(\pm\)0.037) & 99.1(\(\pm\)0.009) & 90.1 (\(\pm\)0.016) & 93.1 (\(\pm\)0.013) & 99.6 (\(\pm\)0.003) \\ \hline Recall & 72.1 (\(\pm\)0.041) & 77.4 (\(\pm\)0.038) & 99.1(\(\pm\)0.009) & 88.9 (\(\pm\)0.016) & 94.0 (\(\pm\)0.012) & 99.5 (\(\pm\)0.004) \\ \hline F1-score & 63.6 (\(\pm\)0.043) & 76.6 (\(\pm\)0.039) & 99.1(\(\pm\)0.009) & 88.5 (\(\pm\)0.017) & 92.5 (\(\pm\)0.014) & 99.8 (\(\pm\)0.002) \\ \hline Accuracy & 61.5 (\(\pm\)0.044) & 66.7 (\(\pm\)0.043) & 99.3 (\(\pm\)0.008) & 90.7 (\(\pm\)0.015) & 93.7 (\(\pm\)0.013) & 99.8 (\(\pm\)0.002) \\ \hline \multicolumn{5}{c}{**GBM**} \\ \hline Precision & 64.0 (\(\pm\)0.043) & 75.6 (\(\pm\)0.039) & 99.4 (\(\pm\)0.007) & 85.0 (\(\pm\)0.019) & 86.5 (\(\pm\)0.018) & 95.3(\(\pm\)0.011) \\ \hline Recall & 66.6 (\(\pm\)0.043) & 74.6 (\(\pm\)0.039) & 99.1 (\(\pm\)0.009) & 82.9 (\(\pm\)0.020) & 88.1 (\(\pm\)0.017) & 94.8(\(\pm\)0.012) \\ \hline F1-score & 62.6 (\(\pm\)0.044) & 72.7 (\(\pm\)0.040) & 99.3 (\(\pm\)0.008) & 82.6 (\(\pm\)0.020) & 85.9 (\(\pm\)0.018) & 96.8(\(\pm\)0.009) \\ \hline Accuracy & 61.5 (\(\pm\)0.044) & 65.5 (\(\pm\)0.043) & 99.3 (\(\pm\)0.008) & 85.4(\(\pm\)0.018) & 86.7(\(\pm\)0.018) & 92.6(\(\pm\)0.014) \\ \hline \multicolumn{5}{c}{**ABC**} \\ \hline Precision & 62.9 (\(\pm\)0.044) & 73.4 (\(\pm\)0.040) & 99.1 (\(\pm\)0.009) & 81.3 (\(\pm\)0.020) & 85.4 (\(\pm\)0.018) & 94.3(\(\pm\)0.012) \\ \hline Recall & 64.1 (\(\pm\)0.044) & 73.7 (\(\pm\)0.040) & 99.4 (\(\pm\)0.007) & 84.0 (\(\pm\)0.019) & 87.5 (\(\pm\)0.017) & 94.5(\(\pm\)0.012) \\ \hline F1-score & 63.1 (\(\pm\)0.044) & 74.1 (\(\pm\)0.040) & 97.9 (\(\pm\)0.013) & 82.6 (\(\pm\)0.020) & 84.5 (\(\pm\)0.019) & 95.9(\(\pm\)0.010) \\ \hline Accuracy & 58.0 (\(\pm\)0.044) & 62.8 (\(\pm\)0.043) & 98.0 (\(\pm\)0.008) & 83.0 (\(\pm\)0.020) & 85.2 (\(\pm\)0.018) & 92.0 (\(\pm\)0.014) \\ \hline \multicolumn{5}{c}{**BSVM**} \\ \hline Precision & 61.4 (\(\pm\)0.044) & 71.4 (\(\pm\)0.041) & 98.7 (\(\pm\)0.010) & 81.0 (\(\pm\)0.020) & 84.1 (\(\pm\)0.019) & 91.1(\(\pm\)0.015) \\ \hline Recall & 61.0 (\(\pm\)0.044) & 72.6 (\(\pm\)0.041) & 99.0 (\(\pm\)0.009) & 82.5 (\(\pm\)0.020) & 82.3 (\(\pm\)0.020) & 92.0(\(\pm\)0.014) \\ \hline F1-score & 61.9 (\(\pm\)0 In particular, the results of precision, recall, F1-score and accuracy per each emotion, learning algorithm and database were subsequently discussed. Table 5 shows that angry, calm and disgust emotions were easier to recognize using RDF and GBM ensemble learning of RAVDESS MFCC1 features. The results were particularly impressive with RDF ensemble learning with perfect precision, recall and F1-score computed by the algorithm, which also recorded highest accuracy values across all emotions. However, BMLP generally gave the least performing result across all emotions. RAVDESS MFCC1 features achieved a precision rate of 92.0% for the fear emotion using RDF ensemble learning, which is relatively high when compared to the results in [27]. Table 6 shows the results of percentage precision, recall, F1-score and accuracy performance analysis of RAVDESS MFCC2 features. The performance values were generally higher for recognizing angry, calm and disgust emotions using RDF. However, the performance values were relatively low for happy and neutral emotions showing that the hybridization of MFCC, ZCR, energy and fundamental frequency features yielded a relatively poor result in recognizing happy and neutral emotions using ensemble learning. The MFCC2 combination of acoustic features achieved an improved recognition performance of the fear (90.0%) emotion using RDF in comparison with the report in [44], where 81.0% recognition was achieved for the fear emotion using the CASIA database.