Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Multi-Level Information and Automatic Dialog Acts Detection in Human-Human Spoken Dialogs

Sophie Rosset, Delphine Tribout

LIMSI-CNRS, Orsay, France

This paper reports on our experience in annotating and the automatically detecting dialog acts in human-human spoken dialog. Our work is based on three hypotheses: first, the dialog act succession is strongly constrained; second, initial word and semantic class of word are more important than the exact word in identifying the dialog act; third, information is encoded in specific entities. We also used historical information in order to account for the dialogical structure. A memory based learning approach is used to detect dialog acts. Experiments have been conducted using different kind of information levels. In order to verify our hypotheses, the model trained on a French corpus was tested on an English corpus for a similar task and on a French corpus from a different domain. A correct dialog act detection rate of about 87% is obtained for the same domain/ language condition and 80% for the cross-language and cross-domain conditions.

Full Paper

Bibliographic reference.  Rosset, Sophie / Tribout, Delphine (2005): "Multi-level information and automatic dialog acts detection in human-human spoken dialogs", In INTERSPEECH-2005, 2789-2792.