Application of nonlinear dynamics characterization to emotional speech

Authors:
Patricia Henríquez;Jesús B. Alons;Miguel A. Ferrer;Carlos M. Travieso;Juan R. Orozco-Arroyave
Affiliations:
Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Ingeniería Electrónica, Universidad de Antioquia, GEPAR and GITA Research Groups, Medellín, Colombia
Venue:
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Year:
2011

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Abstract

This paper proposes the application of nonlinear measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon entropy, Lempel-Ziv complexity and Hurst exponent are extracted from the samples of a database of emotional speech. Then, 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 emotional states). Feature selection is accomplished to select a reduced number of features. In order to evaluate the discrimination ability of the selected features a neural network classifier is used. A global success rate of 93.78% is obtained.