Developing a smart system for binary classification of disordered voices using machine learning.
Journal:
American journal of otolaryngology
Published Date:
Jan 1, 2025
Abstract
OBJECTIVES: Voice disorder is characterized by disruptions in voice quality caused by issues in vocal fold vibration during phonation. The study explored the application of machine learning, based on the Random Forest (RF) and Decision Tree (DT) models, in the classification of normophonic and disordered voices using acoustic features. The RF and DT classifiers were compared, and the diagnostic utility of individual acoustic parameters was evaluated across multilingual databases, with an emphasis on Cantonese voice samples.
