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Updated: Jul 5, 2026

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Published on: November 25, 2017

Have We Solved Glottis Segmentation? Review and Commentary.

Andreas M Kist¹, Michael D Llinger²

¹Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universit.±t Erlangen-N..rnberg (FAU), 91052 Erlangen, Germany.

Journal of Voice : Official Journal of the Voice Foundation

|December 7, 2024

Summary

This summary is machine-generated.

Automated glottis segmentation using deep learning shows promise for voice physiology research. However, challenges remain, indicating continued scientific interest in refining vocal fold motion analysis.

Keywords:

Deep learning..÷Glottis segmentation..÷Vocal folds..÷Quantification..÷Glottal area..÷Image processing..÷Image analysis..÷Deep neural networks

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Area of Science:

Voice physiology and biomechanics.
Medical image analysis and computational modeling.

Background:

Quantifying voice physiology is crucial for understanding vocal fold function.
Glottis segmentation for vocal fold motion analysis has gained attention over the past 20 years.
Full automation of glottis segmentation has been a persistent challenge.

Purpose of the Study:

To highlight ongoing challenges and opportunities in glottis segmentation.
To emphasize the continued scientific relevance of glottis segmentation.
To discuss the future of automated vocal fold motion analysis.

Main Methods:

Review of deep learning approaches for glottis segmentation.
Analysis of current limitations in automated segmentation.
Discussion of open research questions in the field.

Main Results:

Deep learning has enabled near-fully automated glottis segmentation solutions.
Despite advancements, complete automation and robustness are still under development.
Several "open construction sites" remain in glottis segmentation research.

Conclusions:

Glottis segmentation remains a vital area of scientific inquiry for voice research.
Further advancements are needed to fully automate and validate segmentation techniques.
The field requires continued research to enhance the quantification of vocal fold dynamics.