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01.12.2013

Global Selection of Features for Nonlinear Dynamics Characterization of Emotional Speech

verfasst von: Patricia Henríquez Rodríguez, Jesús B. Alonso Hernández, Miguel A. Ferrer Ballester, Carlos M. Travieso González, Juan R. Orozco-Arroyave

Erschienen in: Cognitive Computation | Ausgabe 4/2013

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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 %.

Vorheriger Artikel A Real-Time Speech Enhancement Framework in Noisy and Reverberated Acoustic Scenarios

Nächster Artikel Children’s Emotion Recognition from Spontaneous Speech Using a Reduced Set of Acoustic and Linguistic Features

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Titel: Global Selection of Features for Nonlinear Dynamics Characterization of Emotional Speech
verfasst von: Patricia Henríquez Rodríguez
Jesús B. Alonso Hernández
Miguel A. Ferrer Ballester
Carlos M. Travieso González
Juan R. Orozco-Arroyave
Publikationsdatum: 01.12.2013
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 4/2013
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-012-9157-0

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