A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders

SummaryThe human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. H...

Full description

Saved in:
Bibliographic Details
Published in:Journal of voice 2019-11, Vol.33 (6), p.947.e11-947.e33
Main Authors: Hegde, Sarika, Shetty, Surendra, Rai, Smitha, Dodderi, Thejaswi
Format: Article
Language:English
Subjects:
Automatic detection
Literature survey
Machine learning
Otolaryngology
Pathological voice
Review
Voice disorder
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:SummaryThe human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. Hence, it is paramount to identify voice changes at an early stage and provide the patient with an opportunity to overcome any ramification and enhance their quality of life. In this line of work, automatic detection of voice disorders using machine learning techniques plays a key role, as it is proven to help ease the process of understanding the voice disorder. In recent years, many researchers have investigated techniques for an automated system that helps clinicians with early diagnosis of voice disorders. In this paper, we present a survey of research work conducted on automatic detection of voice disorders and explore how it is able to identify the different types of voice disorders. We also analyze different databases, feature extraction techniques, and machine learning approaches used in these research works.
ISSN:0892-1997
1873-4588
DOI:10.1016/j.jvoice.2018.07.014