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Updated: May 17, 2026

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
Published on: December 2, 2011
Classification of glottic insufficiency and tension asymmetry using a multilayer perceptron
Matthew R Hoffman1, Ketan Surender, Erin E Devine
1Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
Objective:
Laryngeal function can be evaluated from multiple perspectives, including aerodynamic input, acoustic output, and mucosal wave vibratory characteristics. To determine the classifying power of each of these, we used a multilayer perceptron artificial neural network (ANN) to classify data as normal, glottic insufficiency, or tension asymmetry.
Study Design:
Case series analyzing data obtained from excised larynges simulating different conditions.
Methods:
Aerodynamic, acoustic, and videokymographic data were collected from excised canine larynges simulating normal, glottic insufficiency, and tension asymmetry. Classification of samples was performed using a multilayer perceptron ANN.
Results:
A classification accuracy of 84% was achieved when including all parameters. Classification accuracy dropped below 75% when using only aerodynamic or acoustic parameters and below 65% when using only videokymographic parameters.
Conclusions:
Samples were classified with the greatest accuracy when using a wide range of parameters. Decreased classification accuracies for individual groups of parameters demonstrate the importance of a comprehensive voice assessment when evaluating dysphonia.
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