Call classification using recurrent neural networks, support vector machines and finite state automata

Our objective is spoken language classification for helpdesk call routing using a scanning understanding and intelligent system techniques. In particular, we examine simple recurrent networks, support vector machines and finite-state transducers for their potential in this spoken language classification task and we describe an approach to classification of recorded operator assistance telephone utterances. The main contribution of the paper is a comparison of a variety of techniques in the domain of call routing. Support vector machines and transducers are shown to have some potential for spoken language classification, but the performance of the neural networks indicates that a simple recurrent network performs best for helpdesk call routing.

• Type: Journal
• Year: 2006
• Where: KAIS
• Authors: Sheila Garfield, Stefan Wermter
• Keywords: KAIS 2006, Language Classification, Simple Recurrent Network, Spoken Language Classification