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A High-Efficiency Fatigued Speech Feature Selection Method for Air Traffic Controllers Based on Improved Compressed

Yonggang Yan1,2, Yi Mao3, Zhiyuan Shen1

  • 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211016, China.

Journal of Healthcare Engineering
|October 7, 2021
PubMed
Summary

This study introduces an efficient method to detect air traffic controller fatigue using speech analysis. By reducing complex speech features, the new approach significantly improves fatigue detection accuracy.

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Area of Science:

  • Aviation Safety
  • Speech Signal Processing
  • Machine Learning

Background:

  • Air traffic controller fatigue is a major cause of aviation incidents.
  • Current speech-based fatigue detection methods suffer from high-dimensional features, leading to computational inefficiency.
  • Effective feature selection is crucial for practical fatigue detection.

Purpose of the Study:

  • To propose a high-efficiency fatigued speech selection method.
  • To address the challenges of high dimensionality and computational complexity in fatigue detection.
  • To improve the precision of fatigue detection in air traffic controllers.

Main Methods:

  • Developed an improved compressed sensing construction algorithm for superior sparse coding.
  • Applied fractal dimension to optimize high-dimensional fatigued speech features.
  • Utilized a support vector machine classifier for comparative experiments.

Main Results:

  • The proposed method significantly reduces feature dimensionality while maintaining detection accuracy.
  • Achieved superior sparse coding and decreased reconstruction error in fatigued speech.
  • Demonstrated improved precision in fatigue detection compared to existing methods.

Conclusions:

  • The developed method offers a computationally efficient and precise approach to air traffic controller fatigue detection.
  • Optimized speech feature selection is key to advancing aviation safety through technology.
  • This research provides a valuable tool for enhancing air traffic management systems.