Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.

Journal: Journal of medical Internet research
Published Date:

Abstract

BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.

Authors

  • Alper Idrisoglu
    Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden.
  • Ana Luiza Dallora
    Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden.
  • Peter Anderberg
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
  • Johan Sanmartin Berglund
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.