Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estima...

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Bibliographic Details
Published in:Bioengineering (Basel) 2023-03, Vol.10 (4), p.426
Main Authors: Calà, Federico, Frassineti, Lorenzo, Manfredi, Claudia, Dejonckere, Philippe, Messina, Federica, Barbieri, Sergio, Pignataro, Lorenzo, Cantarella, Giovanna
Format: Article
Language:English
Subjects:
Accuracy
acoustical analysis
Bayesian analysis
Bioengineering
BioVoice
Dysphonia
Dystonia
Learning algorithms
LIME
Machine learning
Mathematical models
Model accuracy
Muscle contraction
Muscles
Neural networks
Optimization
Parameter estimation
Patients
Physiological aspects
Professional relationships
Quality
spasmodic dysphonia
Spasms
Speech
Support vector machines
Tremor (Muscular contraction)
voice assessment
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Summary:Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estimated from the Italian word /a'jwɔle/ emitted by 28 female patients, manually segmented from a standardized sentence and used as features in two classification experiments. Subjects were divided into three severity classes (mild, moderate, severe) on the basis of the G (grade) score of the GRB scale. The first aim was that of finding relationships between perceptual and objective measures with the Local Interpretable Model-Agnostic Explanations method. Then, the development of a diagnostic tool for adductor spasmodic dysphonia severity assessment was investigated. Reliable relationships between G; R (Roughness); B (Breathiness); Spasmodicity; and the acoustical parameters: voiced percentage, F2 median, and F1 median were found. After data scaling, Bayesian hyperparameter optimization, and leave-one-out cross-validation, a k-nearest neighbors model provided 89% accuracy in distinguishing patients among the three severity classes. The proposed methods highlighted the best acoustical parameters that could be used jointly with GRB indices to support the perceptual evaluation of spasmodic dysphonia and provide a tool to help severity assessment of spasmodic dysphonia.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering10040426