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Related Experiment Video

Updated: Jun 18, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Using modulation spectra for voice pathology detection and classification.

Maria Markaki1, Yannis Stylianou

  • 1Department of Computer Science, University of Crete, 71409 Crete, Greece. mmarkaki@csd.uoc.gr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces Modulation Spectra combined with Higher Order Singular Value Decomposition (SVD) for effective voice pathology detection and classification. The approach achieved high accuracy in identifying and categorizing various voice disorders.

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Last Updated: Jun 18, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Speech processing
  • Biomedical engineering
  • Machine learning for healthcare

Background:

  • Voice disorders significantly impact quality of life.
  • Accurate detection and classification of voice pathologies are crucial for effective treatment.
  • High-dimensional data from voice analysis can pose challenges for machine learning models.

Purpose of the Study:

  • To investigate the efficacy of Modulation Spectra for voice pathology detection and classification.
  • To reduce the dimensionality of Modulation Spectra features using Higher Order Singular Value Decomposition (SVD).
  • To propose a feature selection algorithm based on Mutual Information for improved classification accuracy.

Main Methods:

  • Utilized Modulation Spectra as the primary feature extraction technique.
  • Applied Higher Order Singular Value Decomposition (SVD) for dimensionality reduction.
  • Developed a feature selection algorithm leveraging Mutual Information between voice quality and extracted features.
  • Employed Support Vector Machines (SVM) with a radial basis function (RBF) kernel for classification.

Main Results:

  • Achieved a 94.1% detection rate and 97.8% Area Under the Curve (AUC) for general voice pathology detection.
  • Attained an average detection rate of 88.6% and AUC of 94.8% for classifying specific pathologies: polyp, keratosis leukoplakia, adductor spasmodic dysphonia, and vocal nodules.

Conclusions:

  • The proposed method combining Modulation Spectra, SVD, and Mutual Information-based feature selection demonstrates high performance in voice pathology detection and classification.
  • This approach offers a robust and accurate tool for the analysis of pathological voices.
  • The findings suggest significant potential for clinical application in diagnosing voice disorders.