# Research on Speech Emotion Recognition Method Based A-CapsNet
## 5 Experimental Results of the Proposed Model A-CapsNet
### Experimental Settings  
All studies are carried out on a powerful PC with 64 GB of RAM with Windows 10. The processing speed is 2.10 GHz, the core is 40, and the logic processor is 80. TensorFlow [40] is used to implement all models. The following are the parameter combinations of the proposed model A-CapsNet: the convolution kernel size is set to 3 \(\times\) 3, the step size is 1,  
Figure 6: Data flow diagram. Figure 7: The dynamic routing process employed in A-CapsNet. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Data** & & & & \multicolumn{4}{c}{**SNR = 10**} & \multicolumn{4}{c}{**SNR = 5**} & \\
**Division** & **Folds** & **1** & **2** & **3** & **4** & **5** & **Avg \(\pm\)std** & **1** & **2** & **3** & **4** & **5** & **Avg \(\pm\)std** \\ \hline EDCV & K\({}_{\text{J,odds}}\) = 5 & 56.76 & 56.95 & 56.95 & 56.64 & 57.13 & 56.89 \(\pm\) 0.03 & 74.14 & 73.02 & 67.66 & 71.84 & 71.84 & 71.70 \(\pm\) 4.81 \\ EDCV & K\({}_{\text{J,odds}}\) = 5 & 57.26 & 58.69 & 58.13 & 56.88 & 56.01 & 57.39 \(\pm\) 0.88 & 73.33 & 70.784 & 70.59 & 71.59 & 69.35 & 71.14 \(\pm\) 1.72 \\ EDCD & 5 times & 56.95 & 56.64 & 57.01 & 58.32 & 58.50 & 57.48 \(\pm\) 0.59 & 73.08 & 73.96 & 71.78 & 73.15 & 73.27 & 73.05 \(\pm\) 0.50 \\ EIRD & 5 times & 58.07 & 57.82 & 55.58 & 59.25 & 57.69 & 57.68 \(\pm\) 1.41 & 72.15 & 72.83 & 72.40 & 73.64 & 72.06 & 72.62 \(\pm\) 0.33 \\ Data & \multicolumn{4}{c}{SNR = 0} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ Division & \multicolumn{4}{c}{Folds} & 1 & 2 & 3 & 4 & 5 & Avg \(\pm\)std & 1 & 2 & 3 & 4 & 5 & Avg \(\pm\)std \\ EDCV & K\({}_{\text{J,odds}}\) = 5 & 56.84 & 58.92 & 58.22 & 89.60 & 87.35 & 88.29 \(\pm\) 1.54 & 96.07 & 96.76 & 96.36 & 96.51 & 96.20 & 96.37 \(\pm\) 0.06 \\ EEXV & K\({}_{\text{J,odds}}\) = 5 & 87.60 & 87.73 & 87.29 & 90.03 & 85.86 & 87.70 \(\pm\) 1.80 & 97.32 & 97.01 & 97.26 & 95.83 & 97.57 & 97.00 \(\pm\) 0.37 \\ EDRD & 5 times & 86.79 & 86.92 & 88.29 & 87.23 & 86.04 & 87.05 \(\pm\) 0.53 & 96.14 & 96.07 & 94.83 & 95.89 & 95.08 \(\pm\) 0.29 \\ ERD & 5 times & 87.91 & 87.98 & 88.29 & 88.10 & 88.10 & 88.08 \(\pm\) 0.02 & 96.14 & 95.76 & 96.88 & 96.45 & 95.95 & 96.24 \(\pm\) 0.16 \\ Data & \multicolumn{4}{c}{SNR = 10} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} & \multicolumn{4}{c}{} \\ Division & \multicolumn{4}{c}{Folds} & 1 & 2 & 3 & 4 & 5 & Avg \(\pm\)std & 1 & 2 & 3 & 4 & 5 & Avg \(\pm\)std \\ EDCV & K\({}_{\text{J,odds}}\) = 5 & 99.44 & 98.88 & 99.07 & 99.31 & 99.44 & 99.23 \(\pm\) 0.05 & 89.32 & 89.89 & 88.90 & 88.87 & 88.05 & 89.01 \(\pm\) 0.36 \\ EICV & K\({}_{\text{J,odds}}\) = 5 & 96.69 & 99.38 & 99.31 & 98.82 & 99.50 & 99.14 \(\pm\) 0.10 & 89.37 & 89.46 & 89.26 & 89.50 & 88.91 & 89.30 \(\pm\) 0.04 \\ EDRD & 5 times & 98.94 & 99.56 & 99.50 & 99.25 & 99.56 & 99.36 \(\pm\) 0.06 & 99.12 & 89.43 & 89.63 & 89.63 & 89.05 & 89.01 \(\pm\) 0.45 \(\pm\) 0.17 \\ EIRD & 5 times & 98.75 & 99.38 & 99.44 & 99.31 & 99.44 & 99.26 \(\pm\) 0.07 & 89.05 & 90.36 & 89.46 & 89.53 & 88.85 & 89.45 \(\pm\) 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of the proposed model A-CapsNet using the database EMODB under different SNRs with the batch size = 128. The EIRD is the acronym for emotion-independent random division. That is, all samples are randomly divided into five equal parts and four parts are selected as training data, while the remaining part is used as test data. The EDRD is the acronym for emotion-dependent random division. That is, the samples of each emotion category are divided into five equal halves.