Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44005
Título: Global Selection of Features for Nonlinear Dynamics Characterization of Emotional Speech
Autores/as: Henríquez Rodríguez, Patricia
Alonso Hernández, Jesús B. 
Ferrer Ballester, Miguel A. 
Travieso González, Carlos M. 
Orozco-Arroyave, Juan R.
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Nonlinear dynamic Emotional speech Forward floating feature selection Neural networks
Fecha de publicación: 2013
Editor/a: 1866-9956
Publicación seriada: Cognitive Computation 
Resumen: This paper proposes the application of measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon’s entropy, Lempel–Ziv complexity and Hurst exponent are extracted from the samples of a database of emotional speech. Then, summary statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Berlin emotional speech database for a three-class problem (neutral, fear and anger as emotional states). Feature selection is accomplished and a methodology is proposed to find the best features. In order to evaluate the discrimination ability of the selected features, a neural network classifier is used. The global success rate is 93.78 ± 3.18 %.
URI: http://hdl.handle.net/10553/44005
ISSN: 1866-9956
DOI: 10.1007/s12559-012-9157-0
Fuente: Cognitive Computation[ISSN 1866-9956],v. 5, p. 517-525
Colección:Artículos
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