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Manufacturing Process for Non-Adhesive Super-Soft Vocal Fold Models
Published on: January 5, 2024
Model-based classification of nonstationary vocal fold vibrations
Tobias Wurzbacher1, Raphael Schwarz, Michael Döllinger
1Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Medical School, Erlangen, Germany.
The Journal of the Acoustical Society of America
|August 31, 2006
Summary
Classifying vocal fold vibrations during pitch changes, not just sustained sounds, improves voice disorder diagnosis. This new method accurately distinguishes normal and disordered voices using real-time vocal fold motion analysis.
Area of Science:
- Laryngology
- Bioacoustics
- Biomechanical modeling
Background:
- Objective voice disorder assessment relies on classifying vocal fold vibrations.
- Conventional methods use sustained phonation, limiting generalizability to natural speech.
- Nonstationary vocal fold vibrations offer richer diagnostic information.
Purpose of the Study:
- To develop and validate a method for classifying vocal fold vibrations during nonstationary phonation (pitch raise).
- To assess the clinical utility of this method in distinguishing normal and dysphonic voices.
Main Methods:
- Real-time vocal fold oscillations were recorded during a pitch raise paradigm.
- Classification utilized asymmetry measures from a biomechanical two-mass vocal fold model.
- The model was adapted to observed vocal fold motion via optimization.
Main Results:
- The model-based classification accurately identified normal and dysphonic voices in all nonstationary phonation cases.
- Classification failed when applied to conventional sustained phonation recordings.
- Nonstationary phonation analysis revealed crucial vocal fold irregularity information.
Conclusions:
- Classifying vocal fold vibrations during nonstationary phonation is superior to sustained phonation for voice disorder assessment.
- This approach provides objective interpretation of voice disorders by capturing dynamic vocal fold behavior.
- The developed method shows high potential for clinical application in voice diagnostics.
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