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Discovering Hidden Vocal Subtypes: An Unsupervised Acoustic-Biomechanical Exploration of Voice Profiles
Margarita Pérez-Bonilla1, Paola Díaz-Borrego2, Marina Mora-Ortiz3
1Physical Medicine & Rehabilitation, Reina Sofía University Hospital, 14004 Córdoba, Spain; Department of Applied Physics, Radiology and Physical Medicine, Faculty of Medicine and Nursing of Córdoba, 14004 Córdoba, Spain.
Summary
This study identified two distinct voice profiles using acoustic and biomechanical data, revealing patterns in voice production that are independent of specific diagnoses like amyotrophic lateral sclerosis (ALS) or dysphonia.
Area of Science:
- Speech and Voice Science
- Biomedical Engineering
- Data Science in Healthcare
Background:
- Voice disorders are often categorized by diagnosis, potentially overlooking underlying functional patterns.
- Understanding the acoustic-biomechanical interplay in voice production is crucial for accurate characterization.
Purpose of the Study:
- To explore latent acoustic-biomechanical patterns in voice production using unsupervised multivariate analysis.
- To identify data-driven vocal profiles in individuals with amyotrophic lateral sclerosis (ALS) and nonneurological dysphonia.
Main Methods:
- Analyzed sustained vowel phonation from 100 individuals (ALS and nonneurological dysphonia).
- Utilized 26 acoustic and biomechanical variables, applying Principal Component Analysis (PCA) and unsupervised clustering.
- Assessed cluster validity and performed post hoc statistical comparisons.
Main Results:
- PCA revealed structured relationships between acoustic and biomechanical features, explaining 70.7% of variance.
- Clustering identified two consistent vocal profiles with significant differences in shimmer, harmonics-to-noise ratio (HNR), and biomechanical parameter Pr11.
- These profiles were not significantly associated with clinical diagnostic categories.
Conclusions:
- Unsupervised multimodal analysis identified two coherent vocal profiles transcending traditional diagnostic labels.
- These data-driven voice phenotypes may capture functional patterns of voice production.
- The findings support refined, personalized characterization of voice disorders.
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