Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer's disease

被引：20

作者：

Lopez-de-Ipina, K. ^{[1
]}

Alonso-Hernandez, J. B. ^{[2
]}

Sole-Casals, J. ^{[3
]}

Travieso-Gonzalez, C. M. ^{[2
]}

Ezeiza, A. ^{[1
]}

Faundez-Zanuy, M. ^{[4
]}

Calvo, P. M. ^{[1
]}

Beitia, B. ^{[5
]}

机构：

[1] Univ Basque Country UPV EHU, Syst Engn & Automat Dept, Donostia San Sebastian 20008, Spain

[2] Univ Las Palmas Gran Canaria, IDeTIC, Inst Desarrollo Tecnol & Innovac Comunicac IDeTIC, Las Palmas Gran Canaria, Spain

[3] Univ Vic Cent Univ Catalonia, Data & Signal Proc Res Grp, Vic, Spain

[4] Escola Univ Politecn Mataro UPC, Barcelona, Spain

[5] Univ Basque Country UPV EHU, Dept Math, Leioa, Spain

来源：

NEUROCOMPUTING | 2015年 / 150卷

关键词：

Emotional response; Automatic speech analysis; Emotion recognition; Non-linear modeling; Fractal dimension; Emotional temperature; FRACTAL APPROACH; DEMENTIA; RECOGNITION; DIMENSION;

D O I：

10.1016/j.neucom.2014.05.083

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：392 / 401

页数：10