Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43974
Título: Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer's disease
Autores/as: López-de-Ipiña, K.
Alonso-Hernández, J. B. 
Solé-Casals, J.
Travieso-González, C. M. 
Ezeiza, A.
Faúndez-Zanuy, M.
Calvo, P. M.
Beitia, B.
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Emotional responseAutomatic speech analysisEmotion recognitionNon-linear modelingFractal dimensionEmotional temperature
Fecha de publicación: 2015
Editor/a: 0925-2312
Publicación seriada: Neurocomputing 
Conferencia: IEEE 17th International Conference on Intelligent Engineering Systems (INES) 
Resumen: 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.
URI: http://hdl.handle.net/10553/43974
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.05.083
Fuente: Neurocomputing[ISSN 0925-2312],v. 150, p. 392-401
Colección:Artículos
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