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.
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Wang, F., Verhelst, W., Sahli, H. (2011). Relevance Vector Machine Based Speech Emotion Recognition. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_12
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DOI: https://doi.org/10.1007/978-3-642-24571-8_12
Publisher Name: Springer, Berlin, Heidelberg
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