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 threeclass 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.
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Henríquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M., Orozco-Arroyave, J.R. (2011). Application of Nonlinear Dynamics Characterization to Emotional Speech. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds) Advances in Nonlinear Speech Processing. NOLISP 2011. Lecture Notes in Computer Science(), vol 7015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25020-0_17
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DOI: https://doi.org/10.1007/978-3-642-25020-0_17
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