## 6 Conclusions and Future Work  
In this research study, a deep learning model was introduced for speech emotion recognition. The effectiveness of the proposed model was assessed using four datasets: EmoDB, RAVDESS, TESS, and SAVEE. The experimental findings demonstrated that the proposed model achieved better results with GWO, irrespective of the model used on the four datasets. The evaluations revealed that the results obtained were comparable to the accuracy of other convolutional neural networks (CNNs) utilizing spectrogram features. Consequently, it can be concluded that the proposed approach is highly suitable for emotion recognition.  
It is recommended to evaluate the proposed approach further using additional emotion datasets. Additionally, the proposed model can be enhanced by utilizing large datasets to improve the recognition of all emotions. Another solution to increase the accuracy of the model could be to combine all four datasets present and extract common emotions from them. This will result in a more balanced dataset that will show promising results based on overall SER instead of individual datasets, with constraints being used for the same objective.  
In the future, human assistance will no longer be necessary for speech recognition tasks as they transition into automated processes. Voice recognition is likely to become a seamless and automatic function. It would not be surprising if we are eventually all carrying earpieces like C-3 PO that listen to our conversations. Ongoing research and development in deep learning are expanding the possibilities of speech recognition. Exciting advancements are being made, including the utilization of neural networks to analyze sound patterns, resulting in improved artificial intelligence algorithms. Speech recognition, a subset of deep learning, has been a subject of study for over five decades. Presently, speech recognition systems exhibit higher accuracy levels than ever before. The future holds even more promise for speech recognition as deep learning techniques enable training models on larger datasets and utilize increased computing power, enhancing the algorithms' data processing capabilities.  
Conceptualization, S.T.; methodology, S.T.; software, S.T.; validation, S.T. and S.S.; formal analysis, S.T.; writing--original draft preparation, S.T.; writing--review and editing, S.S.; visualization, S.T.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: The accuracy and loss by epochs on four different datasets—RAVDESS, SAVEE, TESS, and EMODB, they should be listed as: (5 (**a**–**h**)).  
Data Availability Statement: Publicly available datasets were analyzed in this study. The datasets RAVDESS, TESS, SAVEE, and EmoDB might be available on Kaggle ([[https://www.kaggle.com](https://www.kaggle.com)]([https://www.kaggle.com](https://www.kaggle.com))) (5 November 2023).  
The authors would like to thank the \"Doctoral School of Applied Informatics and Applied Mathematics\" and the \"High Performance Computing Research Group\" of Obuda University for their valuable support. The authors would like to thank NVIDIA Corporation for providing graphics hardware for the experiments.  
The authors declare no conflicts of interest.