ISCA Archive Interspeech 2005
ISCA Archive Interspeech 2005

Multi-level information and automatic dialog acts detection in human-human spoken dialogs

Sophie Rosset, Delphine Tribout

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.


doi: 10.21437/Interspeech.2005-822

Cite as: Rosset, S., Tribout, D. (2005) Multi-level information and automatic dialog acts detection in human-human spoken dialogs. Proc. Interspeech 2005, 2789-2792, doi: 10.21437/Interspeech.2005-822

@inproceedings{rosset05_interspeech,
  author={Sophie Rosset and Delphine Tribout},
  title={{Multi-level information and automatic dialog acts detection in human-human spoken dialogs}},
  year=2005,
  booktitle={Proc. Interspeech 2005},
  pages={2789--2792},
  doi={10.21437/Interspeech.2005-822}
}