## 6 Experiment
### Experimental Results and Analysis
#### 6.6.2 Analysis of Recognition Accuracy
For low-arousal emotions, such as sadness and disgust, the recognition accuracy of the IMel features was higher than that of the Mel features. For sadness, the recognition accuracies of the Mel features were 75.87%, 70.54%, and 66.87%, respectively, and those of the IMel features were 84.68%, 79.35%, and 70.96%, respectively. These results indicate that the IMel spectrogram is more suitable for low-arousal and high-frequency emotions than the Mel spectrogram. (3) ROC curves  
In Figure 12, the horizontal coordinate is the false Positive Rate, and the vertical coordinate is the true positive Rate. AUC refers to the area under the ROC curve. Figure 12a presents the ROC curves of the system for 'anger', 'anxiety', 'boredom', 'disgust', 'happiness', 'neutral','sadness' obtained by our proposed approach to the DBM-DB dataset, the red dashed line is the mean ROC value of each emotion. As can be seen from the figure, the system of this paper has good performance. (4) The performance of the proposed system  
Although the algorithm proposed in this paper has two CNN channels to extract the deep features, and doubles the number of parameters in terms of complexity, we can build two projects in Pycharm for Mel spectrogram and IMel spectrogram at the same time, and therefore there is no change in running time. The dimension of the single channel is 128, that of double channel is 256, and whether the input is 128 or 256 dimensions has little effect on the running time of SSAE. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Emotion** & **Features** & **Anger** & **Happiness** & **Neutral** & **Disgust** & **Sadness** & **Anxiety** & **Fearful** & **Surprise** & **Boredom** & **Calm** & **Average** \\ \hline \multirow{3}{*}{EMO-DB} & Mel & 94.83 & 78.71 & 89.96 & 68.52 & 75.87 & 79.56 & - & 88.16 & - & 82.23 \\  & IMel & 88.71 & 58.67 & 82.29 & 76.56 & 84.68 & 80.50 & - & 81.56 & - & 79.56 \\ \cline{1-1}  & Mel & 80.17 & 76.92 & 76.62 & 68.08 & 70.54 & - & 78.67 & 78.33 & - & - & 75.61 \\ \cline{1-1}  & IMel & 71.67 & 69.67 & 71.32 & 73.45 & 79.35 & - & 75.97 & 71.67 & - & - & 73.30 \\ \cline{1-1}  & [PERSON] & 80.82 & 77.87 & 71.00 & 71.03 & 66.87 & - & 78.76 & 68.14 & - & 77.58 & 74.01 \\ \cline{1-1}  & IMel & 68.79 & 71.23 & 67.47 & 75.58 & 70.96 & - & 69.67 & 74.93 & - & 70.64 & 71.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition Accuracy of Various Emotions (%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Database** & **E** & **Mel** & **IMel** & **Mel + IMel** & **MelSSAE** & **IMelSSAE** & **(Mel + IMel) SSAE** \\ \hline \multirow{3}{*}{EMO-DB} & WA & 80.77 \(\pm\) 7.45 & 63.53 \(\pm\) 9.50 & 78.49 \(\pm\) 9.86 & 84.61 \(\pm\) 7.29 & 76.63 \(\pm\) 8.17 & 84.68 \(\pm\) 5.43 \\  & UA & 80.49 \(\pm\) 4.50 & 61.60 \(\pm\) 10.04 & 74.65 \(\pm\) 4.88 & 82.14 \(\pm\) 6.30 & 73.31 \(\pm\) 7.67 & 81.42 \(\pm\) 4.45 \\ \cline{1-1}  & WA & 68.06 \(\pm\) 7.24 & 48.20 \(\pm\) 7.19 & 87.36 \(\pm\) 7.70 & 71.53 \(\pm\) 6.37 & 52.50 \(\pm\) 7.26 & 71.94\(\pm\) 8.22 \\ \cline{1-1}  & UA & 66.93 \(\pm\) 7.88 & 47.92 \(\pm\) 6.97 & 66.53 \(\pm\) 7.90 & 67.71 \(\pm\) 6.61 & 49.74 \(\pm\) 7.26 & 67.71 \(\pm\) 7.58 \\ \hline \hline \end{tabular}  
E-Evaluation indexes.  
\end{table} 
Table 5: GD-female Recognition Accuracy \(\pm\) Standard Deviations (%). (5) Comparison of various papers  
Table 7 lists the results of the comparison between this paper and related papers published in recent years. The listed papers all use spectrograms to detect emotion. It can be observed that our method of combining the Mel spectrogram and IMel spectrogram is better than methods that use only a Mel spectrogram or a standard spectrogram.