Voice disorder recognition using machine learning: a scoping review protocol.

ABSTRACT:

Introduction

Over the past decade, several machine learning (ML) algorithms have been investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML algorithms can detect voice disorders with high accuracy. This suggests that ML has the potential to assist clinicians in the analysis and treatment outcome evaluation of voice disorders. However, despite numerous research studies, none of the algorithms have been sufficiently reliable to be used in clinical settings. Through this review, we aim to identify critical issues that have inhibited the use of ML algorithms in clinical settings by identifying standard audio tasks, acoustic features, processing algorithms and environmental factors that affect the efficacy of those algorithms.

Methods

We

SUBMITTER: Gupta R 

PROVIDER: S-EPMC10895235 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature