# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 4 Results and Discussion  
The present authors have extracted the set of 404 HAFs from RAVDESS and SAVEE experimental databases. The features were used to train five famous ensemble learning algorithms in order to determine their effectiveness using four standard performance metrics. The performance metrics were selected because the experimental databases were not equally distributed. Data were subjected to 10 fold cross-validation and divided into training and testing groups as the norm permits. Gaussian noise was added to the training data to provide a regularizing effect, reduce overfitting, increase resilience and improve generalization performance of the ensemble learning algorithms. Table 3 shows the result of the CPU computational time of training an individual ensemble learning algorithm with three different sets of features across two experimental databases. The training time analysis shows that RDF spent the least amount of training time when compared to other ensemble learning algorithms. The least training time was recorded with respect to MFCC1 and MFCC2 irrespective of databases. However, the training time of RDF (0.43) was slightly higher than that of BSVM (0.39) with respect to SAVEE HAF, but it claimed superiority with respect to the  
Figure 3: Flowchart for different configurations of emotion recognition methods. RAVDESS HAF. The reason for this inconsistent result might be because of the smaller data size (480) of SAVEE when compared to the size (1432) of RAVDESS. The experimental results of a study that was aimed at fully assessing the predictive performances of SVM and SVM ensembles over small and large scale datasets of breast cancer show that SVM ensemble based on BSVM method can be the better choice for a small scale dataset [78]. Moreover, it can be observed that GBM and BMLP were generally time consuming when compared to the other ensemble learning algorithms because they took 1172.3 milliseconds to fit MFCC2 RAVDESS and 1319.39 milliseconds to fit MFCC2 SAVEE features respectively. The experimental results of the overall recognition performance obtained are given in Table 4. The percentage average recognition performance rates differ depending on critical factors such as the type of database, the accent of the speakers, the way data were collected, the type of features and the learning algorithm used. In particular, RDF consistently recorded the highest average accuracy, while BMLP consistently recorded the lowest average accuracy across the sets of features, irrespective of databases. These results indicate that RDF gave the highest performance because the computed values of the precision, recall, F1-score and accuracy of the ensemble learning algorithm are generally impressive when compared to other ensemble learning algorithms. However, average recognition accuracies of 61.5% and 66.7% obtained by RDF for the SAVEE MFCC1 and MFCC2 were respectively low, but the values were better than that of BMLP (55.4% and 60.7%) for SAVEE MFCC1 and MFCC2 respectively. In general, an improvement can be observed when a combination of features (MFCC2) was used in an experiment drawing inspiration from the work done in [46], where MFCC features were combined with pitch and ZCR features. In addition, we borrowed the same line of thought from [PERSON] and [PERSON] [3] and [PERSON] et al. [28] where some of the above mentioned features were combined to form hybrid features. \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** & **MFCC1** & **MFCC2** & **HAF** & **MFCC1** & **MFCC2** & **HAF** \\ \hline
**RDF** & 0.03 & 0.38 & 0.43 & 0.052 & 1.62 & 0.06 \\ \hline
**GBM** & 2.30 & 579.40 & 343.10 & 5.90 & 1172.30 & 6.90 \\ \hline
**ABC** & 0.47 & 22.16 & 1.38 & 2.59 & 2.79 & 1.89 \\ \hline
**BSVM** & 0.20 & 49.79 & 0.39 & 0.42 & 211.54 & 0.48 \\ \hline
**BMLP** & 3.13 & 1319.39 & 952.46 & 76.00 & 231.16 & 7.18 \\ \hline \hline \end{tabular}
\end{table}