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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Identification of voice disorders using long-time features and support vector machine with different feature

Meisam Khalil Arjmandi1, Mohammad Pooyan, Mohammad Mikaili

  • 1Department of Electrical and Electronic Engineering, Faculty of Engineering, Shahed University, Tehran, Iran. msmarjmandi@gmail.com

Journal of Voice : Official Journal of the Voice Foundation
|December 28, 2010
PubMed
Summary

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Early diagnosis of voice disorders is crucial. Acoustic analysis combined with statistical pattern recognition, specifically Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), effectively identifies voice disorders with high accuracy.

Area of Science:

  • Medical acoustics
  • Biomedical signal processing
  • Statistical pattern recognition

Background:

  • Early identification of voice disorders is essential for timely intervention and preventing critical conditions.
  • Acoustic analysis offers a non-invasive complementary method to traditional techniques like laryngoscopy for voice disorder diagnosis.

Purpose of the Study:

  • To investigate the efficacy of statistical pattern recognition techniques for diagnosing voice disorders.
  • To propose and evaluate a combined scheme of feature reduction and pattern recognition methods for voice disorder classification.

Main Methods:

  • Employed feature reduction techniques including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
  • Utilized six different classifiers to evaluate feature vectors derived from reduction methods.

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  • Examined feature selection methods: individual, forward, backward, and branch-and-bound.

    • Main Results:

    • The combination of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) achieved the highest performance, with a recognition rate of 94.26% and an Area Under the Curve (AUC) of 97.94%.
    • This LDA-SVM architecture demonstrated the lowest complexity among the evaluated methods.
    • Individual feature selection combined with SVM yielded a recognition rate of 91.55% and an AUC of 95.80%.

    Conclusions:

    • The proposed combined scheme of feature reduction and pattern recognition, particularly LDA with SVM, is highly effective for accurate voice disorder classification.
    • Statistical pattern recognition provides a valuable tool for non-invasive voice disorder diagnosis.
    • The study highlights the potential of optimized feature selection and classification for improving diagnostic accuracy in voice disorders.