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Beyond Global Metrics: A Fairness Analysis for Interpretable Voice Disorder Detection Systems.

Mariel Estevez1, Cyntia Bonomi2, Dayana Ribas3

  • 1Instituto de Investigación en Ciencias de la Computación (ICC), UBA-CONICET, Pabellón Cero+Infinito - Ciudad Universitaria, Buenos Aires C1428EGA, Argentina.

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Automatic voice disorder detection (AVDD) systems show demographic biases, misclassifying older healthy individuals and younger disordered ones. Group-specific calibration improves reliability by addressing these performance disparities.

Keywords:
Automatic voice disorder detectionCalibrationDemographic-dependent biases

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Area of Science:

  • Biomedical Engineering
  • Speech Science
  • Artificial Intelligence in Healthcare

Background:

  • Automatic Voice Disorder Detection (AVDD) systems are crucial for diagnosing speech impairments.
  • Existing AVDD systems often exhibit performance disparities across different demographic groups.
  • Global performance metrics may mask underlying biases in AVDD system accuracy.

Purpose of the Study:

  • To investigate and quantify demographic-dependent biases in AVDD systems.
  • To evaluate the effectiveness of group-specific calibration strategies for enhancing AVDD reliability.
  • To identify factors contributing to performance differences across age and gender cohorts.

Main Methods:

  • Analysis of an AVDD system using voice disorder datasets with demographic metadata.
  • Evaluation of system performance across gender and age-based cohorts using normalized costs and cross-entropy.
  • Application of group-specific calibration techniques to mitigate miscalibration.

Main Results:

  • Significant performance disparities were observed across demographic groups, despite strong overall metrics.
  • The AVDD system exhibited biases, misclassifying older healthy speakers and younger disordered speakers.
  • Group-specific calibration improved posterior probability estimation and reduced overconfidence.

Conclusions:

  • Global performance metrics are insufficient for a fair evaluation of AVDD systems.
  • Demographic-specific analysis and calibration are essential for reliable voice disorder detection.
  • The findings provide a framework for improving bias mitigation in biomedical classification tasks.