Relevance vector machine based speech emotion recognition

Authors:
Fengna Wang;Werner Verhelst;Hichem Sahli
Affiliations:
Vrije Universiteit Brussel, AVSP, Department ETRO VUB-ETRO, Brussels, Belgium;Vrije Universiteit Brussel, AVSP, Department ETRO VUB-ETRO, Brussels, Belgium and Interdisciplinary Institute for Broadband Technology - IBBT, Ghent, Belgium;Vrije Universiteit Brussel, AVSP, Department ETRO VUB-ETRO, Brussels, Belgium and Interuniversity Microelectronics Centre - IMEC VUB-ETRO, Brussels, Belgium
Venue:
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Year:
2011

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Abstract

This work aims at investigating the use of relevance vector machine (RVM) for speech emotion recognition. The RVM technique is a Bayesian extension of the support vector machine (SVM) that is based on a Bayesian formulation of a linear model with an appropriate prior for each weight. Together with the introduction of RVM, aspects related to the use of SVM are also presented. From the comparison between the two classifiers, we find that RVM achieves comparable results to SVM, while using a sparser representation, such that it can be advantageously used for speech emotion recognition.