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Related Experiment Video

Updated: Jun 18, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Advanced differential evolution for gender-aware English speech emotion recognition.

Liya Yue1, Pei Hu2, Jiulong Zhu3

  • 1Fanli Business School, Nanyang Institute of Technology, Nanyang, 473004, China.

Scientific Reports
|July 31, 2024
PubMed
Summary

This study introduces a gender-based speech emotion recognition (SER) system. By developing distinct models for male and female speakers and optimizing features with an advanced differential evolution algorithm (ADE), the system significantly improves recognition accuracy.

Keywords:
Differential evolutionEmotion recognitionGender

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Speech emotion recognition (SER) systems face challenges due to gender differences and numerous extracted features, impacting recognition efficiency.
  • Existing SER models often struggle to account for variations in speech patterns between genders, leading to suboptimal performance.
  • The large number of features extracted from speech signals can also contribute to decreased recognition accuracy and increased computational load.

Purpose of the Study:

  • To develop a novel SER system that addresses gender-specific variations in speech for improved emotion recognition accuracy.
  • To introduce an advanced differential evolution algorithm (ADE) for optimal feature selection in SER, enhancing model performance.
  • To investigate the impact of gender-specific emotion recognition models on overall system effectiveness.

Main Methods:

  • A SER system was designed incorporating gender recognition and emotion classification models.
  • Distinct emotion recognition models were developed for male and female speakers, with gender identification preceding emotion classification.
  • An advanced differential evolution algorithm (ADE), featuring new difference vectors, mutation operators, position learning, and a novel repairing method, was employed for optimal feature selection.

Main Results:

  • Experiments on four English datasets demonstrated the superiority of the proposed ADE algorithm over comparison methods.
  • The ADE-based SER system achieved higher recognition accuracy, recall, precision, and F1-score.
  • The system also showed improvements in the number of features used and execution time, highlighting its efficiency.

Conclusions:

  • Gender is a significant factor in refining speech emotion recognition models, and gender-specific approaches enhance performance.
  • The proposed advanced differential evolution algorithm (ADE) effectively optimizes feature selection for SER, balancing global and local searches.
  • Mel-frequency cepstral coefficients (MFCCs) were identified as important factors contributing to gender differences in speech emotion recognition.