Global Selection of Features for Nonlinear Dynamics Characterization of Emotional Speech

ABSTRACT: 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 %.

• Autores: Orozco Arroyave, Juan Rafael; Henríquez Rodríguez, Patricia; Alonso Hernández, Jesús Bernardino; Ferrer Ballester, Miguel Ángel; Travieso González, Carlos Manuel
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2013
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/35812
• Acceso en línea: https://hdl.handle.net/10495/35812
• Palabra clave: Nonlinear Dynamics; Dinámicas no Lineales; Expressed Emotion; Emoción Expresada; Expressed Emotion; Neural networks; Redes de neuronas; http://aims.fao.org/aos/agrovoc/c_37467
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/2.5/co/