Comparing support vector machines, recurrent networks, and finite state transducers for classifying spoken utterances

Authors:
Sheila Garfield;Stefan Wermter
Affiliations:
University of Sunderland, School of Computing and Technology, Centre for Hybrid Intelligent Systems, Sunderland, United Kingdom;University of Sunderland, School of Computing and Technology, Centre for Hybrid Intelligent Systems, Sunderland, United Kingdom
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 8
Cited 0

Making large-scale support vector machine learning practical

Advances in kernel methods
Hybrid neural plausibility networks for news agents

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Speaking in time

Speech Communication - Dialogue and prosody
Information Retrieval

Information Retrieval
Finite-State Language Processing

Finite-State Language Processing
Recurrent Neural Learning for Helpdesk Call Routing

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Speech and Language Processing (2nd Edition)

Speech and Language Processing (2nd Edition)
The generalization error of the symmetric and scaled support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes new experiments for the classification of recorded operator assistance telephone utterances. The experimental work focused on three techniques: support vector machines (SVM), simple recurrent networks (SRN) and finite-state transducers (FST) using a large, unique telecommunication corpus of spontaneous spoken language. A comparison is made of the performance of these classification techniques which indicates that a simple recurrent network performed best for learning classification of spontaneous spoken language in a robust manner which should lead to their use in helpdesk call routing.