# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 5 Conclusions  
The automatic recognition of valence emotion in speech is still an open research agenda because of various factors that include differences in the expression of emotions according to different cultures, accents, and languages. Limited research has been done in cross-language emotion recognition with an insufficient number of cross-corpora for research, as seen in the literature review section of this paper. The corpus of this study was developed by scaling down low-discriminating features from the HAF features that were constructed in the previous study through the agglutination of spectral and prosodic features. The feature scaling was done through the selection of highly discriminative features with the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **EMO-DB** & **SAVEE** & **RAVDEES** & **EMOVO** & **CREMA-D** \\ \hline Before RFE & 3.440 & 2.070 & 6.190 & 2.540 & 29.200 \\ After RFE & 0.780 & 0.470 & 1.380 & 0.580 & 6.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Training time (ms) of RADOG on individual corpus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corpus** & **Stage** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
**EMO-DB** & **Before RFE** & 95 & 96 & 95 & 95 \\  & **After RFE** & 97 & 96 & 96 & 96 \\ \hline
**SAVEE** & **Before RFE** & 97 & 96 & 96 & 96 \\  & **After RFE** & 98 & 98 & 98 & 98 \\ \hline
**RAVDEES** & **Before RFE** & 94 & 94 & 94 & 95 \\  & **After RFE** & 97 & 97 & 97 & 97 \\ \hline
**EMOVO** & **Before RFE** & 92 & 92 & 93 & 92 \\  & **After RFE** & 94 & 94 & 94 & 95 \\ \hline
**CREMA-D** & **Before RFE** & 88 & 89 & 88 & 89 \\  & **After RFE** & 85 & 95 & 89 & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Percentage precision, recall and F1-score and accuracy of RADOG on individual corpus at each experimental stage of feature scaling with the RF-RFE algorithm. aid of the RF-RFE algorithm. The scaling down of the redundant features has achieved excellent results in this study. The features were extracted from a cross-language speech emotion corpus that was developed in this study by fusing five different speech emotion corpora. The development of a RADOG stacked ensemble algorithm for recognizing cross-language valence emotion is an important highlight of this study. The algorithm was evaluated against other top performing learning algorithms, and it outperformed them before and after feature scaling. RADOG performed well because it combines strong learners to boost its performance. Furthermore, we have observed generally that ensemble algorithms perform well for the assignment of valence speech emotion recognition. The results of this study are generally promising when compared to the results presented in the literature reviewed. The gradient boosting machine achieved good results, but the greatest bottleneck is that its training time is computationally expensive. However, XGB had the fastest training time, making it a good alternative for developing real-time valence emotion recognition systems. Even though the RADOG algorithm was not the fastest, it had a healthy training time and becomes a good candidate when considering the tradeoff between method efficiency and method accuracy. RADOG was seen to be the more accurate algorithm when compared to random decision forest algorithm in recognizing valence emotion through speech using a set of well-scaled HAF features. This superiority was consistent across all the ensemble algorithms. The results obtained are generally exhilarating; nonetheless, the core limitation of this study is that the cross-language corpora used did not contain conversations, but rather consisted of sentences per speaker. We would like to evaluate the performance of RADOG algorithm in actual conversations to fully scrutinize its robustness in future work. Although the data used in this study were free of noise, they were also devoid of speakers with physiological deficiencies such as stammering.