Paper Info
Title
Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer's disease.
Abstract
Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.05.083
Neurocomputing
Keywords
DocType
Volume
Emotional response,Automatic speech analysis,Emotion recognition,Non-linear modeling,Fractal dimension,Emotional temperature
Journal
150
ISSN
Citations 
PageRank 
0925-2312
2
0.36
References 
Authors
14
8
Name
Order
Citations
PageRank
Karmele López de Ipiña113025.69
Jesús Bernardino Alonso Hernández2222.11
Jordi Solé-Casals38223.24
Carlos Manuel Travieso-González4225.37
Aitzol Ezeiza510117.59
Marcos Faúndez-Zanuy632941.14
P. M. Calvo762.86
Blanca Beitia872.50