Intelligent Simulation and Design of Voice-Based Emotion Recognition Using Optimized Computing Methods
Keywords:
Voice Emotion Recognition, Speech Signal Processing, CNN-LSTM, Deep Learning, Emotion Classification, Optimized Computing, Simulation, Feature Extraction, MFCC, Real-time Emotion DetectionAbstract
In recent years, the ability to accurately detect human emotions from speech has gained significant attention due to its applications in human-computer interaction, virtual assistants, healthcare, and security systems. This research focuses on the intelligent simulation and design of a voice-based emotion recognition system using optimized computing methods. The study explores speech signal processing techniques and implements advanced machine learning and deep learning algorithms—including CNN, LSTM, and a hybrid CNN-LSTM model—for accurate emotional state classification. Using benchmark datasets such as RAVDESS and TESS, the system was trained and tested after thorough feature extraction using MFCC, pitch, and spectral features. The proposed hybrid CNN-LSTM model achieved superior performance with an accuracy of 89.7% and an average AUC of 0.92, outperforming traditional approaches. Simulation experiments also confirmed the system's effectiveness in real-time environments. The results demonstrate the feasibility and efficiency of deploying intelligent voice emotion recognition in real-world applications.
Downloads
References
Chamishka, S., Madhavi, I., Nawaratne, R., Alahakoon, D., De Silva, D., Chilamkurti, N., & Nanayakkara, V. (2022). A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling. Multimedia Tools and Applications, 81(24), 35173-35194.
Davletcharova, A., Sugathan, S., Abraham, B., & James, A. P. (2015). Detection and analysis of emotion from speech signals. Procedia Computer Science, 58, 91-96.
Deepika, T. I., & Sigappi, A. N. (2024, December). Multi-Model Emotion Recognition from Voice Face and Text Sources using Optimized Progressive Neural Network: A Literature Review. In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 1245-1253). IEEE.
Gomathy, M. (2021). Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm. International Journal of Speech Technology, 24(1), 155-163.
Jason, C. A., & Kumar, S. (2020). An appraisal on speech and emotion recognition technologies based on machine learning. language, 67, 68.
Laukka, P., Elfenbein, H. A., Thingujam, N. S., Rockstuhl, T., Iraki, F. K., Chui, W., & Althoff, J. (2016). The expression and recognition of emotions in the voice across five nations: A lens model analysis based on acoustic features. Journal of personality and social psychology, 111(5), 686.
Li, X., Shi, X., Hu, D., Li, Y., Zhang, Q., Wang, Z., ... & Akagi, M. (2023). Music theory-inspired acoustic representation for speech emotion recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing.
Mano, L. Y., Mazzo, A., Neto, J. R., Meska, M. H., Giancristofaro, G. T., Ueyama, J., & Júnior, G. A. (2019). Using emotion recognition to assess simulation-based learning. Nurse education in practice, 36, 13-19.
Mohmad, G. H., & Delhibabu, R. (2024). Speech Databases, speech features and classifiers in speech emotion recognition: A Review. IEEE Access.
Parvathi, M., Pranathi, V., Varma, M., & Satyanarayana, B. V. V. (2024, April). Voice-based smart system for emotion recognition and regulation. In International Conference on Cognitive Computing and Cyber Physical Systems (pp. 474-488). Cham: Springer Nature Switzerland.
Saraswathi, R. V., Nandigama, D., Vasavi, R., Babu, B. G., & Shivani, D. S. (2021, October). Voice based emotion detection using deep neural networks. In 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1-6). IEEE.
Sudhakar, R. S., & Anil, M. C. (2015, February). Analysis of speech features for emotion detection: A review. In 2015 International Conference on Computing Communication Control and Automation (pp. 661-664). IEEE.
Zhuang, J. R., Guan, Y. J., Nagayoshi, H., Muramatsu, K., Watanuki, K., & Tanaka, E. (2019). Real-time emotion recognition system with multiple physiological signals. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 13(4), JAMDSM0075-JAMDSM0075.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
