Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Cross-Domain Classification Using Generalized Domain Acts

Andrew N. Pargellis, Alexandros Potamianos

Bell Labs, Lucent Technologies, Murray Hill, NJ, USA

Cross-domain classification for speech understanding is an interesting research problem because of the need for portable solutions in the design for spoken dialogue systems. In this paper, a two-tier classifier is proposed for speech understanding. The first tier consists of domain independent dialogue acts while the second tier consists of application actions that are domain specific. A maximum likelihood and a minimum classification error formulation are proposed for the first tier of the classifier, i.e., for dialogue act classification. The performance of the classifier is investigated for three application domains. Cross-domain classification error is two to four times higher than in-domain classification error. A 10-15% reduction in cross-domain classification error rate is achieved by adding generic domain independent training data for each dialogue act and by mapping words to semantic concepts.

Full Paper

Bibliographic reference.  Pargellis, Andrew N. / Potamianos, Alexandros (2000): "Cross-domain classification using generalized domain acts", In ICSLP-2000, vol.3, 502-505.