# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 4 Results and Discussion  
In this study, negative and positive valence emotion files were extracted from the speech corpora of EMO-DB, SAVEE, RAVDESS, EMOVO, and CREMA-D to create the cross-language corpus according to the mapping in [25]. Thereafter, 250 of the most discriminating HAF features were selected from the speech files for the assignment of valence speech emotion recognition. The effectiveness of the proposed RALOG algorithm was compared with other popular ensemble algorithms trained to learn the extracted HAF features. Cross-language speech data used for experimentation were subjected to 10-fold cross-validation and divided into training and testing groups as the standard norm permits. Inthe experimentation, 20% of the data (sets of adult utterances) were used for testing, while 80% of the data were used for training as previously mentioned. Table 5 shows the central processing unit (CPU) time of training the individual ensemble algorithm investigated in this study. The GBM algorithm was seen to be the most time-consuming, which was greatly reduced after performing feature selection. It took 60.495 ms to train the GBM algorithm before running the RF-RFE algorithm, but it only took 16.228 ms after applying the feature selection algorithm. This pattern was consistent for all the other learning algorithms, including the proposed RALOG. In addition, the results show that the RALOG algorithm had a healthy training time of 9.05 ms. However, the XGB algorithm proved to be the fastest amongst the learning algorithms because it only took 0.00095 ms to process data. These results imply that RALOG is highly promising for recognizing valence speech emotion, especially in the situation where reactionary delay is of crucial importance [55]. Reactionary delay is an important factor in the development of real-time systems. Figure 6: Different configurations of valence recognition experiments derived from [46]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **RALOG** & **RDF** & **GBM** & **ETC** & **XGB** \\ \hline Before RFE & 43.314 & 5.290 & 60.495 & 0.168 & 0.004 \\ After RFE & 9.050 & 0.459 & 16.228 & 0.244 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training time (ms) of ensemble algorithms. The experimental results obtained in this study in terms of precision, recall, F1-score, and accuracy are illustrated in Table 6. The results show that feature selection with the aid of the RF-RFE algorithm contributed heavily to a significant increase in the recognition accuracy. The recognition accuracy of RALOG increased from 82% to 92% while that of XGB algorithm increased from 78% to 87%. The increase in accuracy was apparent in all results of the learning algorithms. However, the highest increment was observed when the RF-RFE algorithm was used with the ETC learning algorithm, which has resulted in a 10% increment. Even though the ETC learning algorithm recorded the highest increment, it has yielded a lower accuracy score when compared to other learning algorithms. It can be observed from results in Table 6 that the RDF algorithm has performed well in recognizing valence speech emotions because it achieved a recognition accuracy score of 85%. In the same vein, the results show that the ETC algorithm is a good option for recognizing valence speech emotion because it achieved a recognition accuracy of 83% after applying the RF-RFE algorithm. RALOG achieved the highest accuracy before and after the application of the RF-RFE feature selection algorithm. It even performed better when compared to the other algorithms presented in the literature. For instance, [PERSON] et al. (2015) achieved an overall accuracy of 80% using eGeMAPS, DBN, and leave-one-out testing technique. Hence, it can be inferred that RALOG is highly promising for recognizing valence speech emotions, especially when it is used in conjunction with the RF-RFE algorithm. Moreover, the confidence factor shows that the true recognition accuracies of all learning algorithms lie in the range of 0.005% and 0.009%.