Call Classification using Recurrent Neural Networks, Support Vector Machines and Finite State Automata
Knowledge and Information Systems: An International Journal,
Volume 9,
Number 2,
pages 131--156,
doi: 10.1007/s10115-005-0198-5
- Feb 2006
Associated documents :
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.
@Article{GW06,
author = {Garfield, Sheila and Wermter, Stefan},
title = {Call Classification using Recurrent Neural Networks, Support Vector Machines and Finite State Automata},
journal = {Knowledge and Information Systems: An International Journal},
number = {2},
volume = {9},
pages = {131--156},
year = {2006},
month = {Feb},
publisher = {Springer},
doi = {10.1007/s10115-005-0198-5},
}