## 4 Methodology
### Mutation Strategies
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Main Parameters** \\ \hline DE & beta\_min = 0.2; beta\_max = 0.8; pCR = 0.2; \\ BBO\_SCA & KeepRate = 0.2; pMutation = 0.1; w = 0.9; c1 = 2; c2 = 2; \\ SCA & thres = 0.5; \\ MDE & beta\_min = 0.2; beta\_max = 0.8; pCR = 0.2; \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main parameters setting. Figure 5: The classification errors of the compared algorithms based on KNN. It is evident from the figure that MDE excels DE, BBO_PSO, and SCA by achieving errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS. Regarding the maximum error, MDE outperforms DE, BBO_PSO, and SCA in eNTERFACE05, SAVEE, and TESS, while SCA beats DE, BBO_PSO, and MDE in RAVDESS. In terms of the minimum error, DE and SCA perform well in SAVEE and eNTERFACE05, respectively, while BBO_PSO has the best performance in RAVDESS and TESS. It can be found that the data obtained by MDE have excellent stability, which is especially suitable for speech emotion recognition. The Wilcoxon rank-sum test reveals that the algorithms have similar experimental results in SAVEE (as shown in Table 2), and it cannot distinguish the experimental results of DE and MDE in RAVDESS. DE, BBO_PSO, SCA, and MDE perform well on two, one, one, and four datasets, respectively. The Friedman test exhibits that their average ranks are 2.5, 3, 75, 2.75, and 1, with \(p\) < 0.05. Table 2 proves that MDE is superior to other algorithms. Table 3 illustrates the number of selected features and the running time of the algorithms. MDE obtains the least number of selected features and the shortest running time in eNTERFACE05 and TESS, while SCA outperforms DE, BBO_PSO, and MDE in RAVDESS and SAVEE. Their running time in eNTERFACE05 and SAVEE is the lowest, but they spend a lot of time in RAVDESS and TESS. MDE uses the fewest features to complete recognition in eNTERFACE05 and TESS, while it obtains more features than other algorithms in RAVDESS and SAVEE. As can be seen from Figure 5, the recognition accuracy of MDE is better than DE, BBO_PSO, and SCA. From the number of selected features and classification errors obtained by the algorithms, it can be concluded that using more or fewer features is not beneficial for emotion prediction. Based on the above discussion, the proposed MDE exhibits the best performance in classification accuracy and running time, and it is suitable for English speech emotion recognition. #### 5.2.2 Simulation Results on the Random Forest Classifier

Figure 6 displays the experimental results using the RF classifier, and it shows the average, maximum, and minimum errors acquired from each independent run. The errors obtained with RF are superior to the values obtained by KNN. Figure 6 illustrates that MDE has the best results in eNTERFACE05, RAVDESS, SAVEE, and TESS. Its errors in the four emotion datasets are 0.4721, 0.4264, 0.3283, and 0.0114, and it outperforms DE, BBO_PSO, and SCA. In the maximum error, MDE performs well in RAVDESS, SAVEE, and TESS, while DE beats BBO_PSO, SCA, and MDE in eNTERFACE05. Concerning the minimum error, MDE exhibits the best performance in eNTERFACE05, SAVEE, and TESS, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **DE** & **BBO\_PSO** & **SCA** & **MDE** \\ \hline \(>\)/=/\(<\) & 0/2/2 & 0/1/3 & 0/1/3 & 4/0/0 \\ Rank & 2.5 & 3.75 & 2.75 & 1 \\ \(p\)-Value & 2.56 \(\times 10^{-2}\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The non-parametric statistical analysis of the compared algorithms based on KNN.