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An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection.

Rodrigo Colnago Contreras1, Monique Simplicio Viana2, Everthon Silva Fonseca2

  • 1Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil.

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Summary

This study introduces a novel machine learning framework for detecting voice dysphonia, a condition affecting voice biometrics. The proposed method effectively identifies vocal alterations, improving the reliability of voice-based authentication systems.

Keywords:
cepstral analysisdysphonia detectionmachine learningpattern recognitionvoice disorder detection

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Signal Processing

Background:

  • Biometric authentication systems increasingly rely on voice recognition for security.
  • Dysphonia, a vocal disorder, can impair voice biometrics, leading to authentication failures.
  • Developing automatic detection techniques for dysphonia is crucial for robust voice-based security.

Purpose of the Study:

  • To propose a new framework for detecting dysphonia in voice signals.
  • To enhance the reliability of voice biometrics by addressing dysphonia-related inaccuracies.
  • To leverage machine learning for automatic identification of vocal alterations.

Main Methods:

  • Utilizing multiple projections of cepstral coefficients for voice signal representation.
  • Analyzing various cepstral coefficient extraction techniques and fundamental frequency measures.
  • Evaluating the representation capacity on three distinct machine learning classifiers.
  • Conducting experiments on a subset of the Saarbruecken Voice Database.

Main Results:

  • The proposed framework demonstrates effectiveness in detecting the presence of dysphonia.
  • Cepstral coefficients, when combined with fundamental frequency measures, show significant representation capacity.
  • The machine learning classifiers successfully identified dysphonic alterations in voice samples.

Conclusions:

  • The developed framework offers a promising approach for automatic voice dysphonia detection.
  • This research contributes to improving the accuracy and security of voice biometric systems.
  • Further research can explore broader applications of this technique in clinical settings and security.