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

Conference contribution


Publication Details

Author(s): Vasquez-Correa JC, Orozco-Arroyave JR, Arias-Londono JD, Vargas-Bonilla JF, Avendano LD, 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.



FAU Authors / FAU Editors

Nöth, Elmar Prof. Dr.-Ing.
Professur für Informatik (Mustererkennung)


How to cite

APA:
Vasquez-Correa, J.C., Orozco-Arroyave, J.R., Arias-Londono, J.D., Vargas-Bonilla, J.F., Avendano, L.D., & 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.

BibTeX: 

Last updated on 2018-19-04 at 03:19