## 4 Methodology
### Mutation Strategies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**DE**} & \multicolumn{2}{c}{**BBO\_PSO**} & \multicolumn{2}{c}{**SCA**} & \multicolumn{2}{c}{**MDE**} \\ \cline{2-10}  & **Length** & **Time** & **Length** & **Time** & **Length** & **Time** & **Length** & **Time** \\ \hline eNTERFACE05 & 65.85 & 274.2602 & 65.05 & 259.1495 & 66.15 & 255.6974 & **15.85** & **221.556** \\ RAVDESS & 72.35 & 626.1439 & 67.75 & 660.178 & **31.8** & **357.027** & 74.2 & 666.1531 \\ SAVEE & 66.35 & 294.514 & 66.9 & 267.3497 & **28.2** & **238.9834** & 68.55 & 374.099 \\ TESS & 70.5 & 1666.3398 & 68.55 & 1726.7875 & 73.45 & 1837.7258 & **28.8** & **832.355** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of selected features and the running time (seconds) of the compared algorithms based on KNN. and SCA outperforms DE, BBO_PSO, and MDE in RAVDESS. The data obtained by the RF classifier certify that the performance of MDE is stable, and it can be used for English speech emotion recognition. Table 4 presents their non-parametric statistical analysis. Through the Wilcoxon rank-sum test, DE, BBO_PSO, SCA, and MDE perform well on three, two, two, and four datasets, respectively. MDE is superior to the other algorithms, while BBO_PSO and SCA exhibit comparable performance in RF. DE and MDE yield similar results in eNTERFACE05, SAVEE, and TESS, and the Wilcoxon rank-sum test cannot distinguish the experimental data of BBO_PSO, SCA, and MDE in eNTERFACE05 and TESS. Their average ranks are 2.5, 3.5, 3, and 1, and the Friedman test reveals that MDE wins first place, followed by DE, SCA, and BBO_PSO. Table 4 confirms the superiority of MDE in speech emotion recognition. Table 5 illustrates the number of selected features and the running time of the algorithms. The RF classifier provides them with a greater number of features and a longer running time than the KNN classifier. SCA performs well in the number of selected features and running time, and it uses 28.8, 26.4, 20.4, and 38.6 features for classification in the four datasets, respectively. MDE, DE, and BBO_PSO employ approximately half of the features to accomplish emotion recognition. The number of features obtained by MDE in eNTERFACE05 and TESS is smaller than DE, while DE performs better than MDE and BBO_PSO in RAVDESS and SAVEE. The algorithms have more running time in eNTERFACE05 and SAVEE, while they have less time in RAVDESS and TESS. From the experimental results, it can be noticed that although the algorithms obtain different results on KNN and RF classifiers, MDE consistently performs the best. RF has a higher time complexity than KNN, but it utilizes more features to achieve excellent recognition results. \begin{table}
\begin{tabular}{c c c c c} \hline  & **DE** & **BBO\_PSO** & **SCA** & **MDE** \\ \hline \(>\)/\(=\)/\(<\) & 0/3/1 & 0/2/2 & 0/2/2 & 4/0/0 \\ Rank & 2.5 & 3.5 & 3 & 1 \\ \(p\)-Value & 3.84 \(\times 10^{-2}\) & & & \\ \hline \end{tabular}
\end{table} 
Table 4: The non-parametric statistical analysis of the compared algorithms. Figure 6: The classification errors of the compared algorithms. ## 6 Conclusions

In SER, researchers focus on identifying significant emotional features through feature selection; however, searching for the optimal features is impractical due to its high complexity. In this study, we use an improved differential evolution to classify English languages from speech signals based on MFCCs and pitch features. Compared with DE, BBO_PSO, and SCA, the experimental results and non-parametric statistical analysis in the four English speech emotion datasets illustrate that MDE achieves excellent recognition accuracy and reduces the number of selected features. The proposed algorithm works with three mutation strategies and a jumping method to balance global search and local search, and improves the accuracy of speech emotion by reconstructing input speech data with relevant and meaningful acoustic features.