# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 4 Results and Discussion
RALOG and ETC both achieved a precision score of 93% in recognizing instances of negative valence. However, RALOG achieved the highest accuracy score of 97% in recognizing instances of negative valence emotion. Furthermore, RALOG had the highest precision score of 87% in recognizing instances of positive valence. The results in Table 8 confirm the effectiveness of applying the RF-RFE algorithm for cross-language valence speech emotion recognition. These results show a significant improvement in valence emotion recognition because they are comparable to the work presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Stage** & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-score**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline  & & **Negative** & **Positive** & **Negative** & **Positive** & **Negative** & **Positive** & **Negative** & **Positive** \\ \hline
**RALOG** & **Before RFE** & 88 & 76 & 86 & 79 & 88 & 77 & 86 & 78 \\  & **After RFE** & 93 & 87 & 94 & 86 & 94 & 87 & 97 & 86 \\ \hline
**RDF** & **Before RFE** & 83 & 59 & 84 & 78 & 82 & 67 & 82 & 74 \\  & **After RFE** & 97 & 86 & 97 & 62 & 88 & 87 & 92 & 72 \\ \hline
**GBM** & **Before RFE** & 81 & 56 & 78 & 64 & 80 & 62 & 79 & 71 \\  & **After RFE** & 92 & 66 & 89 & 74 & 90 & 70 & 91 & 79 \\ \hline
**XGB** & **Before RFE** & 84 & 60 & 85 & 79 & 83 & 71 & 84 & 76 \\  & **After RFE** & 95 & 61 & 88 & 82 & 91 & 70 & 94 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage precision, recall and F1-score and accuracy at each experimental stage of feature scaling with the RF-RFE algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Valence** & \multicolumn{2}{c}{**Before RFE**} & \multicolumn{2}{c}{**After RFE**} \\ \hline  & & **Negative** & **Positive** & **Negative** & **Positive** \\ \hline
**RALOG** & **Negative** & 88 & 12 & 93 & 7 \\ \cline{2-6}  & **Positive** & 24 & 76 & 13 & 87 \\ \hline
**RDF** & **Negative** & 83 & 17 & 97 & 3 \\ \cline{2-6}  & **Positive** & 41 & 59 & 38 & 62 \\ \hline
**GBM** & **Negative** & 81 & 19 & 92 & 8 \\ \cline{2-6}  & **Positive** & 41 & 59 & 34 & 66 \\ \hline
**ETC** & **Negative** & 80 & 20 & 93 & 7 \\ \cline{2-6}  & **Positive** & 43 & 57 & 37 & 63 \\ \hline
**XGB** & **Negative** & 84 & 16 & 95 & 5 \\ \cline{2-6}  & **Positive** & 40 & 60 & 39 & 61 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix before and after feature scaling with the RF-RFE algorithm. The ROC curves of the evaluated learning algorithms before and after applying the RF-RFE algorithm are shown in Figures 7 and 8, respectively. Figure 7 shows the performance of the ensemble algorithms before the feature selection was done. It can be noted from the ROC curves that RALOG outperformed all the other algorithms before and after the RF-RFE algorithm was applied. RALOG had the highest AUC score of 96.97% when the RF-RFE algorithm was applied. This means that there is a 96.97% chance that correct unpleasant valence emotion will be recognized using RALOG with the RF-RFE algorithm. It was closely followed by GBM, which achieved an AUC score of 94.62%. These results show that the selected features by the RF-RFE algorithm can faithfully recognize valence speech emotion from different languages, accents, and recording environments because the corpus used for experimentation in this study was a  
The final experiment was performed to determine the performance results of the RADOG algorithm on an individual corpus. Table 9 shows these results, wherein it can be seen that the recognition results are relatively good across corpora. Specifically, RADOG gave the best recognition performance results for SAVEE, because an overall accuracy of 98% was recorded. The best performance of RADOG on SAVEE is because the corpus only consists of male utterances from Britain. A reduction in the recognition performance results is observed as we investigate the gender-balanced corpora.