Deep Learning for Emotional Speech Recognition

MÁXIMO E. SÁNCHEZ-GUTIÉRREZ; ENRIQUE M. ALBORNOZ; FABIOLA MARTÍNEZ LICONA; HUGO L. RUFINER; JOHN GODDARD-CLOSE

Cancún

Conferencia; 6th Mexican Conference on Pattern Recognition (MCPR 2014); 2014

Computer Science Department of the National Institute for Astrophysics Optics and Electronics (INAOE) and the Computer Science Department of the Autonomous University of Puebla (BUAP); with the consent of the Mexican Association for Computer Vision, Neu

Emotional speech recognition is a multidisciplinary research area that has received increasing attention over the last few years. The present paper considers the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) to the difficult task of automatic Spanish emotional speech recognition. The principal motivation lies in the success reported in a growing body of work employing these techniques as alternatives to traditional methods in speech processing and speech recognition. Here a well-known Spanish emotional speech database is used in order to extensively experiment with, and compare, different combinations of parameters and classifiers. It is found that with a suitable choice of parameters, RBM and DBN can achieve comparableresults to other classifiers.