Deep Learning for Emotional Speech Recognition

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

Pattern Recognition

Springer International Publishing

Año: 2014; p. 311 - 320

p, li { white-space: pre-wrap; }(Proceedings of 6th Mexican Conference, MCPR 2014, Cancun, Mexico, June 25-28, 2014)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.