Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis.
Journal:
Journal of voice : official journal of the Voice Foundation
PMID:
36376192
Abstract
OBJECTIVES: Machine learning (ML) methods allow the development of expert systems for pattern recognition and predictive analysis of intervention outcomes. It has been used in Voice Sciences, mainly to discriminate between healthy and dysphonic voices. Parameter patterns of vocal acoustic analysis and vocal perceptual assessment can be evaluated by ML classifiers, such as the Fuzzy Triangular Naive Bayes (FTriangNB), after using techniques that improve the vocal quality of individuals with healthy or dysphonic voices. Thus, the goal of this study was to analyze the performance of the FTriangNB to detect patterns in the acoustic parameters and the auditory-perceptual assessment of 12 women with dysphonia and 12 vocally healthy women, after performing three vocal exercises (tongue trills, semi-occluded vocal tract exercise with a high-resistance straw - SOVTE, and over-articulation).
Authors
Keywords
Acoustics
Adult
Bayes Theorem
Case-Control Studies
Dysphonia
Female
Fuzzy Logic
Humans
Machine Learning
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Signal Processing, Computer-Assisted
Speech Acoustics
Speech Perception
Speech Production Measurement
Treatment Outcome
Voice Quality
Voice Training
Young Adult