A Survey on Emotion Speech Recognition Using Support Vector Machine

Authors

  • Mayuri Madhav Belan Student, Department of Computer Science of Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India
  • Nalini Shivaji Kamble Student, Department of Computer Science of Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India
  • Mayuri Mahadev Padulkar Student, Department of Computer Science of Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India
  • R. S. Barwade Professor, Department of Computer Science of Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India

Keywords:

Emotion speech recognition, SVM Classification

Abstract

Recognizing basic emotion through speech is the process of recognizing the intellectual state. Emotion identification through speech is an area which increasingly attracts attention within the engineers in the field of pattern recognition. Emotions play an extremely important role in human life. It is an important medium of expressing a human's viewpoint or feelings and his or her mental state to others. Humans have the natural ability to recognize emotions through speech information. Emotional computing has gained enormous research interest in the development of Human Computer Interaction over the past ten years. With the increasing power of emotion recognition, a logical computer system can provide a more friendly and effective way to communicate with users in areas such as video surveillance, interactive entertainment, intelligent automobile system and medical diagnosis. In this project, our approach is to classify emotions using Support Vector Machine classifiers. Recognition accuracy for these features is considered as it mimics the human ear perception. So emotion recognition using these features are illustrated.

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Published

2021-07-17

How to Cite

[1]
M. M. Belan, N. S. Kamble, M. M. Padulkar, and R. S. Barwade, “A Survey on Emotion Speech Recognition Using Support Vector Machine”, IJRESM, vol. 4, no. 7, pp. 169–171, Jul. 2021.

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Articles