## 7 Conclusions  
In this paper, the frequency domain of IMel was proposed, and an IMel spectrogram that can highlight the high-frequency part was formed, which makes up for the shortage of Mel spectrograms that only highlight the low-frequency part. Then, a CNN-SSAE deep optimization network was presented. In this network, the two-dimensional spectrogram was sent to the CNN to obtain one-dimensional deep features, and then the internal redundant features were compressed and optimized by SSAE. The EMO-DB, SAVEE, and RAVDESS speech databases were used for experiments to verify the complementarity of the Mel spectrogram and IMel spectrogram and the effectiveness of CNN-SSAE in optimizing deep features. According to the current research situation, Although the proposed results obtained better recognition results, However, it also has limitations. The IMel filter is only the inverse process of the Mel filter, highlighting the high-frequency part. In the next step, wavelet algorithm, Hilbert-Huang transform, etc., can be used to highlight different frequency components of the layered signal; therefore, the next step will be to realize new spectrograms to obtain better recognition results. At present, the research tends to be multimodal, combining the results of spectrogram with EEG features or kinematic features. In addition, the next step will be to extract better features through a deep optimized network, and obtain better recognition results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Literature** & **Years** & **Spectrogram** &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{5}{*}{EMO-DB} & [PERSON] et al. [8] & 2020 & Standard spectrogram & 92.02 \\  & [PERSON] et al. [9] & 2020 & [PERSON] spectrogram & 86.92 \\  & [PERSON] et al. [12] & 2016 & Standard spectrogram & 88.40 \\  & Our Method & - & [PERSON] and IMel spectrogram & 94.79 \\  & [PERSON] et al. [9] & 2020 & Mel spectrogram & 75.00 \\  & [PERSON] et al. [11] & 2019 & Mel spectrogram & 81.05 \\  & [PERSON] et al. [20] & 2018 & Standard spectrogram & 75.90 \\  & Our Method & - & [PERSON] and IMel spectrogram & 88.96 \\  & [PERSON] et al. [21] & 2019 & Standard spectrogram & 64.48 \\  & [PERSON] et al. [22] & 2019 & [PERSON] spectrogram & 68.10 \\  & [PERSON] et al. [23] & 2019 & Standard spectrogram & 79.50 \\  & Our Method & - & [PERSON] and IMel spectrogram & 83.18 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of various papers in recent years.  
Figure 12: (a) ROC curve of the system for EMO-DB. (b) ROC curve of the system for SAVEE. (c) ROC curve of the system for RAVDESS.  
**Author Contributions:** Conceptualization, J.L. and X.Z.; methodology, X.Z.; software, L.H.; validation, X.Z., F.L. and Y.S.; formal analysis, J.L.; investigation, S.D.; resources, X.Z. and S.D.; data curation, Y.S. and F.L.; writing--original draft preparation, J.L. and X.Z.; writing--review and editing, J.L., L.H. and S.D.; visualization, Y.S.; supervision, F.L.; project administration, J.L.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Nature Science Foundation of China under Grant 62271342, in part by \"Project 1331\" Quality Enhancement and Efficiency Construction Plan National First-class Major Construction Project of Electronic Science and Technology, in part by the National Natural Science Foundation of China Youth Science Foundation under Grant 12004275, in part by the Natural Science Foundation of Shanxi Province, China, under Grant 201901D111096, in part by Research Project Supported by Shanxi Scholarship Council of China HGKY2019025.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.