Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression

Katie Matton, Purvaja Balaji, Hamzeh Ghasemzadeh, Jameson Cooper, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, Rosalind Picard, John Guttag, S. Mazdak Abulnaga
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1360-1375, 2026.

Abstract

Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician’s expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automatically classifying phonotrauma severity from vocal fold images. To account for the ordinal nature of the labels, we adopt a widely used ordinal regression framework. To account for label uncertainty, we propose a novel modification to ordinal regression loss functions that enables them to operate on soft labels reflecting annotator rating distributions. Our proposed soft ordinal regression method achieves predictive performance approaching that of clinical experts, while producing well-calibrated uncertainty estimates. By providing an automated tool for phonotrauma severity assessment, our work can enable large-scale studies of phonotrauma, ultimately leading to improved clinical understanding and patient care.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-matton26a, title = {Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression}, author = {Matton, Katie and Balaji, Purvaja and Ghasemzadeh, Hamzeh and Cooper, Jameson and Mehta, Daryush D. and Van Stan, Jarrad H. and Hillman, Robert E. and Picard, Rosalind and Guttag, John and Abulnaga, S. Mazdak}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1360--1375}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/matton26a/matton26a.pdf}, url = {https://proceedings.mlr.press/v297/matton26a.html}, abstract = {Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician’s expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automatically classifying phonotrauma severity from vocal fold images. To account for the ordinal nature of the labels, we adopt a widely used ordinal regression framework. To account for label uncertainty, we propose a novel modification to ordinal regression loss functions that enables them to operate on soft labels reflecting annotator rating distributions. Our proposed soft ordinal regression method achieves predictive performance approaching that of clinical experts, while producing well-calibrated uncertainty estimates. By providing an automated tool for phonotrauma severity assessment, our work can enable large-scale studies of phonotrauma, ultimately leading to improved clinical understanding and patient care.} }
Endnote
%0 Conference Paper %T Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression %A Katie Matton %A Purvaja Balaji %A Hamzeh Ghasemzadeh %A Jameson Cooper %A Daryush D. Mehta %A Jarrad H. Van Stan %A Robert E. Hillman %A Rosalind Picard %A John Guttag %A S. Mazdak Abulnaga %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-matton26a %I PMLR %P 1360--1375 %U https://proceedings.mlr.press/v297/matton26a.html %V 297 %X Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician’s expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automatically classifying phonotrauma severity from vocal fold images. To account for the ordinal nature of the labels, we adopt a widely used ordinal regression framework. To account for label uncertainty, we propose a novel modification to ordinal regression loss functions that enables them to operate on soft labels reflecting annotator rating distributions. Our proposed soft ordinal regression method achieves predictive performance approaching that of clinical experts, while producing well-calibrated uncertainty estimates. By providing an automated tool for phonotrauma severity assessment, our work can enable large-scale studies of phonotrauma, ultimately leading to improved clinical understanding and patient care.
APA
Matton, K., Balaji, P., Ghasemzadeh, H., Cooper, J., Mehta, D.D., Van Stan, J.H., Hillman, R.E., Picard, R., Guttag, J. & Abulnaga, S.M.. (2026). Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1360-1375 Available from https://proceedings.mlr.press/v297/matton26a.html.

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