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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Speech Emotion Recognition Based on Modified ReliefF.

Guo-Min Li1, Na Liu1, Jun-Ao Zhang1

  • 1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710600, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a modified ReliefF algorithm to reduce high-dimensional features for improved emotion recognition accuracy. The new method enhances efficiency and performance in human-computer interaction systems.

Keywords:
emotion recognitionfeature selectionmaximum information coefficientmodified ReliefF

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Emotion recognition is crucial for natural human-computer interaction and advancing artificial intelligence.
  • High-dimensional feature sets in current emotion recognition models hinder classification performance and efficiency.

Purpose of the Study:

  • To propose a modified ReliefF feature selection algorithm for reducing feature dimensions.
  • To enhance the accuracy and efficiency of emotion recognition by selecting optimal feature subsets.

Main Methods:

  • Modified the ReliefF algorithm by adjusting random sample selection ranges.
  • Utilized the maximum information coefficient to measure feature correlation.
  • Developed a sample distance measurement method based on feature correlation.

Main Results:

  • Successfully screened feature subsets with reduced dimensions from high-dimensional data.
  • Demonstrated significant improvements in emotion recognition accuracy on eNTERFACE'05 and SAVEE datasets.
  • Validated the effectiveness of the modified algorithm in enhancing classification performance.

Conclusions:

  • The modified ReliefF algorithm effectively reduces data dimensions while improving emotion recognition accuracy.
  • This approach offers a more efficient and accurate solution for emotion recognition in human-computer interaction.
  • The proposed feature selection method has significant implications for developing intelligent systems.