Automatic assessment of voice quality using machine learning

Gradient boosting methods, particularly LightGBM, produced near-expert agreement with perceptual ratings, supporting their potential as objective, interpretable tools for clinical dysphonia assessment.

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Publication Details

Journal
Logopedics Phoniatrics Vocology
Published
2026-02-25
DOI
https://doi.org/10.1080/14015439.2026.2628250
Primary Topic
Voice and Speech Disorders
Type
article
Field-Weighted Citation Impact
0.00
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article

Automatic assessment of voice quality using machine learning

Manwa L. Ng, Yat Chun Au, Nan Yan
Logopedics Phoniatrics Vocology
Voice and Speech Disorders
article

Automatic assessment of voice quality using machine learning

Manwa L. Ng, Yat Chun Au, Nan Yan
article en

Abstract

Gradient boosting methods, particularly LightGBM, produced near-expert agreement with perceptual ratings, supporting their potential as objective, interpretable tools for clinical dysphonia assessment.

Logopedics Phoniatrics Vocology
Chinese Academy of Sciences (CN), Education University of Hong Kong (HK), Shenzhen Institutes of Advanced Technology (CN), University of Hong Kong (HK)
Openalex Percentile: Top 42%
Voice and Speech Disorders
DOISourceOpenAlex
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