Performance Evaluation of Speech Emotion Recognition with Conventional Neural Network

Authors

  • Sana Fatema N. Ali  ME Scholar, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India
  • Prof. S. T. Khandare   Associate Professor, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India
  • Prof. S. Y. Amdani  Associate Professor, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India

Keywords:

Speech emotion, Deep learning, CNN, MATLAB

Abstract

The realm of speech emotion recognition presents a formidable challenge, offering valuable insights into the emotional states of speakers and facilitating enhanced human-machine interactions. However, in various scenarios, particularly those involving resource-constrained environments like embedded systems, the need arises to discern emotions in speech while grappling with limited computing and memory resources. While some prior research has shown promising recognition rates through transfer learning techniques utilising popular models such as Alex Net, a significant hindrance remains their substantial model size, rendering them impractical for execution on embedded systems. In response to this challenge, we present an innovative solution: a compact deep convolutional neural network architecture tailored to address the demands of resource-constrained environments.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-12-30

Issue

Volume 9, Issue 6, November-December-2023

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Research Articles

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How to Cite

[1]
Sana Fatema N. Ali, Prof. S. T. Khandare , Prof. S. Y. Amdani, " Performance Evaluation of Speech Emotion Recognition with Conventional Neural Network" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.37-47, November-December-2023.