## 6 Experiment
### Experimental Results and Analysis
#### 6.6.2 Analysis of Recognition Accuracy  
(1) Recognition results of each experimental scenarios  
The sampling frequency of EMO-DB is 16,000 Hz, and that of SAVEE and RAVDESS is 44,200 Hz. Although the sampling frequencies are different, the frequency range of the speech signal is 200-3400 Hz. For all types of datasets, the energy is mainly concentrated in the range of 0-4000 Hz and there is some energy in the range of 4000-8000 Hz in some cases. From the above analysis, \(R_{\text{max}}\) = 4000 Hz and \(R_{\text{max}}\) = 8000 Hz were compared, In addition, to assess the influence of the number of filters M in the filter bank, M = 40, M = 60, and M = 80 were compared. Table 2 shows that when \(R_{\text{max}}\) = 8000 Hz, M = 60 and E = WA, the highest recognition rate of the EMO-DB database was 94.79 + 1.77%, that of the SAVEE database was 88.96 + 3.56%, and that of the RAVDESS database was 83.18 + 2.31%. This indicates that the frequency range of 0-8000 Hz reflects more comprehensive information. When the number M of filters was equal to 60, the decomposition of the spectrogram was more appropriate. Therefore, the following experiments for SI, GD and various types of emotion analysis were conducted with \(R_{\text{max}}\) = 8000 Hz and M = 60. As seen from Tables 2-5:  
1. The recognition accuracy of the Mel feature was higher than that of IMel, and the effect of using IMel alone was not as good as that of using Mel alone. 2. MelSSAE, IMelSSAE, and (Mel + IMel) achieved higher recognition accuracy than Mel, IMel, and Mel + IMel, respectively, thereby proving the effectiveness of the SSAE in dimension reduction. 3. The recognition accuracy of Mel + IMel was not necessarily higher than that of Mel or IMel, because directly splicing the two may contain redundancy and affect the recognition accuracy. However, after SSAE optimization, (Mel + IMel) SSAE improved the recognition accuracy of Mel, IMel, and Mel + IMel, thereby proving that Mel and IMel are complementary in deep features. 4. The standard deviation value is the average value that describes the distance that each recognition result deviates from the average. The values in the SI environment (Table 2) were less than the values in the SD and GD environments (Tables 3-5). This is because SI took all samples of one subject as the test set and other people's samples as the training set, to reflect the similarity between individuals and groups. For example, GD-male took samples of all male subjects as the training set and samples of one female subject as the test set. To discover the similarity between different genders, there were large differences between the different genders, so the standard deviation of the recognition results was greater in the SI environment. Figure 11: (**a**) Correlation coefficient diagram of the Mel deep features. (**b**) Correlation coefficient diagram of the IMel deep features. (**c**) Correlation coefficient diagram of the Mel + IMel deep features. \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{4}{*}{EMO-DB} & WA & 83.53 \(\pm\) 5.42 & 67.63 \(\pm\) 8.86 & 80.78 \(\pm\) 5.17 & 89.10 \(\pm\) 4.64 & 74.97 \(\pm\) 6.82 & 92.52 \(\pm\) 3.90 \\  & UA & 83.60 \(\pm\) 5.86 & 66.16 \(\pm\) 9.72 & 79.06 \(\pm\) 5.55 & 87.24 \(\pm\) 6.72 & 72.31 \(\pm\) 8.24 & 91.39 \(\pm\) 3.80 \\  & WA & 68.17 \(\pm\) 7.80 & 54.88 \(\pm\) 5.54 & 68.38 \(\pm\) 9.87 & 74.42 \(\pm\) 10.59 & 61.53 \(\pm\) 3.80 & 76.08 \(\pm\) 7.56 \\  & UA & 66.35 \(\pm\) 8.90 & 51.71 \(\pm\) 7.01 & 65.76 \(\pm\) 10.56 & 71.68 \(\pm\) 8.61 & 57.51 \(\pm\) 4.55 & 73.11 \(\pm\) 8.89 \\ \multirow{4}{*}{RAVDESS} & WA & 68.83 \(\pm\) 7.81 & 53.35 \(\pm\) 10.75 & 67.91 \(\pm\) 8.60 & 73.33 \(\pm\) 5.45 & 56.72 \(\pm\) 8.15 & 74.86 \(\pm\) 5.79 \\  & UA & 67.00 \(\pm\) 8.67 & 52.96 \(\pm\) 9.95 & 65.70 \(\pm\) 8.405 & 70.81 \(\pm\) 7.77 & 55.09 \(\pm\) 8.74 & 71.55 \(\pm\) 5.65 \\ \hline \end{tabular}
\end{table}