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- Vol. 5 Issue 3
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A Deep Learning Framework for Real-Time Speech Emotion Recognition
Authors
1 Associate Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering, Bapuji Institute of Engineering , Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 537-542
- Article Type
- Original Article
References
1. S. R. Livingstone and F. A. Russo, “The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English,” PLOS ONE, vol. 13, no. 5, 2018.
2. K. Dupuis and M. K. Pichora-Fuller, “Toronto Emotional Speech Set (TESS),” 2010.
3. S. Mirsamadi, E. Barsoum, and C. Zhang, “Automatic speech emotion recognition using recurrent neural networks with local attention,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2227–2231.
4. K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724–1734.
5. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. M. McFee et al., “librosa: Audio and music signal analysis in Python,” in Proceedings of the Python in Science Conference, 2015, pp. 18–25.
7. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, 2016.
8. L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579–2605, 2008.
9. A. Niculescu-Mizil and R. Caruana, “Predicting good probabilities with supervised learning,” in Proceedings of the International Conference on Machine Learning, 2005, pp. 625–632.
2. K. Dupuis and M. K. Pichora-Fuller, “Toronto Emotional Speech Set (TESS),” 2010.
3. S. Mirsamadi, E. Barsoum, and C. Zhang, “Automatic speech emotion recognition using recurrent neural networks with local attention,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2227–2231.
4. K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724–1734.
5. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. M. McFee et al., “librosa: Audio and music signal analysis in Python,” in Proceedings of the Python in Science Conference, 2015, pp. 18–25.
7. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, 2016.
8. L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579–2605, 2008.
9. A. Niculescu-Mizil and R. Caruana, “Predicting good probabilities with supervised learning,” in Proceedings of the International Conference on Machine Learning, 2005, pp. 625–632.
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