Spoken emotion recognition using deep learning

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

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Springer International Publishing

Año: 2014; p. 104 - 111

(Proceedings of 19th Iberoamerican Congress, CIARP 2014, Puerto Vallarta, Mexico, November 2-5, 2014.)Spoken emotion recognition is a multidisciplinary research area that has received increasing attention over the last few years. In this paper, restricted Boltzmann machines and deep belief networks are used to classify emotions in speech. The motivation lies in the recent success reported using these alternative techniques in speech processing and speech recognition. This classifier is compared with a multilayer perceptron classifier, using spectral and prosodic characteristics. A well-known German emotional database is used in the experiments and two methodologies of cross-validation are proposed. Our experimental results show that the deep method achieves an improvement of 8.67% over the baseline in a speaker independent scheme.