Conference contribution


Time Dependent ARMA for Automatic Recognition of Fear-Type Emotions in Speech


Publication Details
Author(s): Vasquez-Correa J, Orozco-Arroyave J, Arias-Londono J, Vargas-Bonilla J, Avendano L, Nöth E
Publisher: Springer-verlag
Publication year: 2015
Volume: 9302
Pages range: 96-104
ISSN: 0302-9743

Abstract

The speech signals are non-stationary processes with changes in time and frequency. The structure of a speech signal is also affected by the presence of several paralinguistics phenomena such as emotions, pathologies, cognitive impairments, among others. Non-stationarity can be modeled using several parametric techniques. A novel approach based on time dependent auto-regressive moving average (TARMA) is proposed here to model the non-stationarity of speech signals. The model is tested in the recognition of "fear-type" emotions in speech. The proposed approach is applied to model syllables and unvoiced segments extracted from recordings of the Berlin and enterface05 databases. The results indicate that TARMA models can be used for the automatic recognition of emotions in speech.



How to cite
APA: Vasquez-Correa, J., Orozco-Arroyave, J., Arias-Londono, J., Vargas-Bonilla, J., Avendano, L., & Nöth, E. (2015). Time Dependent ARMA for Automatic Recognition of Fear-Type Emotions in Speech. (pp. 96-104). Springer-verlag.

MLA: Vasquez-Correa, J. C., et al. "Time Dependent ARMA for Automatic Recognition of Fear-Type Emotions in Speech." Springer-verlag, 2015. 96-104.

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