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Automated Electroglottographic Inflection Events Detection. A Pilot Study.

Juliana Codino1, María Eugenia Torres2, Adam Rubin3

  • 1Laboratorio de Señales y Dinámicas no Lineales, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Paraná, Entre Ríos, Argentina.

Journal of Voice : Official Journal of the Voice Foundation
|January 23, 2016
PubMed
Summary
This summary is machine-generated.

An automatic method identifies and classifies vocal fold vibration events from electroglottography (EGG) signals. This tool offers quantitative analysis for voice disorders, complementing standard evaluations.

Keywords:
Automated reportContact phaseElectroglottographyInflection eventsVoice

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Area of Science:

  • Biomedical Engineering
  • Speech Science
  • Acoustics

Background:

  • Vocal-fold vibration analysis is crucial for understanding voice production and disorders.
  • Electroglottography (EGG) noninvasively measures glottal impedance changes reflecting vocal fold vibration.
  • Specific EGG signal morphologies, like the 'knee,' indicate vibratory patterns, but variations require detailed analysis.

Purpose of the Study:

  • To develop and validate an automatic computational method for identifying and classifying inflection events in EGG signals.
  • To provide clinicians with a quantitative tool for analyzing vocal fold vibratory patterns.

Main Methods:

  • A computational algorithm was developed based on the mathematical properties of EGG signals to detect events during the contact phase.
  • Retrospective analysis of EGG signals from individuals with voice disorders was conducted.
  • The algorithm's performance was compared against manual evaluations by expert clinical voice analysts and speech pathologists.

Main Results:

  • The automatic method demonstrated a significant level of agreement with human raters in identifying and classifying EGG signal events.
  • The algorithm successfully detected and reported events throughout the contact phase of the vibratory cycle.

Conclusions:

  • The developed automatic tool can provide objective, quantitative, and qualitative reports on EGG signal events.
  • This method can assist speech pathologists in clinical voice evaluations and voice research by offering complementary data.
  • The tool reduces the need for manual EGG signal analysis, saving time for professionals.