A Data-Driven Machine Learning Framework for Speech Emotion Classification

Authors

  • Mrs M. Asha Aruna Sheela|Dr A.Balaji|Mrs Sk. Raziya Sultana| P. Sita Maha Lakshmi

Keywords:

Speech Emotion Recognition, Voice Processing Feature Extraction , Noise Reduction, MFCC (Mel-Frequency Cepstral Coefficients) Emotion Detection from Speech Convolutional Neural Network (CNN) Speech Emotion Recognition (SER) Model Accuracy

Abstract

Speech Emotion Recognition (SER) is a fascinating yet complex area withinhuman-computer interaction, focusing on identifying emotional states through speech. Itleverages vocal attributes such as tone and pitch

References

Mittal, R., Vart, S., Shokeen, P; Kumar, M. (2022). Speech Emotion Recognition.

Aggarwal, A., Srivastava, A., Agarwal, A., Chahal, N., Singh, D., Alnuaim, A. A., Alhadlaq, A., & Lee, H. N. (2022). Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning. Sensors, 22(6).

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Published

2025-06-27

How to Cite

Mrs M. Asha Aruna Sheela|Dr A.Balaji|Mrs Sk. Raziya Sultana| P. Sita Maha Lakshmi. (2025). A Data-Driven Machine Learning Framework for Speech Emotion Classification . Journal of Computational Analysis and Applications (JoCAAA), 34(6), 187–197. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3071

Issue

Vol. 34 No. 6 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.