International Journal of Hybrid Intelligent Systems

 Volume 2, No. 1(2005),  pp. 13 - 33

Spoken Language Classification Using Hybrid Classifier Combination
Sheila Garfield, Stefan Wermter and Siobhan Devlin 

Abstract: In this paper, we describe an approach for spoken language analysis for helpdesk call routing using a combination of
simple recurrent networks and support vector machines. In particular we examine this approach for its potential in a difficult
spoken language classification task based on recorded operator assistance telephone utterances. We explore simple recurrent
networks and support vector machines using a large, unique telecommunication corpus of spontaneous spoken language. The
main contribution of the paper is a combination of techniques in the domain of call routing. First, we find that simple recurrent
networks perform better than support vector machines for this task. Second, we claim that the combination of simple recurrent
networks and support vector machines provides slightly improved performance compared to the performance of either simple
recurrent networks or support vector machines.

Keywords: Classification, spontaneous language, dialogue, recurrent neural networks, support vector machines

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