Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70324
Title: Alzheimer's disease and automatic speech analysis: A review
Authors: Barragán Pulido, María Luisa
Alonso Hernández, Jesús Bernardino
Ferrer Ballester, Miguel Ángel
Travieso González, Carlos Manuel
Mekyska, Jiří
Smékal, Zdeněk
UNESCO Clasification: 320507 Neurología
3314 Tecnología médica
Keywords: Alzheimer'S Disease (AD)
Automatic Processing
Speech
Voice
Issue Date: 2020
Journal: Expert Systems with Applications 
Abstract: The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently used data repositories are also given. Likewise, it identifies the most clinically relevant results and the current lines developed in the field. Automatic speech analysis, within the Health 4.0 framework, offers the possibility of assessing these patients, without the need for a specific infrastructure, by means of non-invasive, fast and inexpensive techniques as a complement to the current diagnostic methods.
URI: http://hdl.handle.net/10553/70324
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2020.113213
Source: Expert Systems With Applications [ISSN 0957-4174], v. 150, 113213, (Julio 2020)
Appears in Collections:Articles
Show full item record
Unknown (1,58 MB)

SCOPUSTM   
Citations

89
checked on Jan 5, 2025

WEB OF SCIENCETM
Citations

72
checked on Jan 5, 2025

Page view(s)

247
checked on Dec 28, 2024

Download(s)

1,416
checked on Dec 28, 2024

Google ScholarTM

Check


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.