IEEE Transactions on Pattern Analysis and Machine Intelligence
Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Segmented and unsegmented dialogue-act annotation with statistical dialogue models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Statistical framework for a Spanish spoken dialogue corpus
Speech Communication
Journal of Artificial Intelligence Research
A study of a segmentation technique for dialogue act assignation
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
SWITCHBOARD: telephone speech corpus for research and development
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Hi-index | 0.01 |
In dialogue systems it is important to label the dialogue turns with dialogue-related meaning. Each turn is usually divided into segments and these segments are labelled with dialogue acts (DAs). A DA is a representation of the functional role of the segment. Each segment is labelled with one DA, representing its role in the ongoing discourse. The sequence of DAs given a dialogue turn is used by the dialogue manager to understand the turn. Probabilistic models that perform DA labelling can be used on segmented or unsegmented turns. The last option is more likely for a practical dialogue system, but it provides poorer results. In that case, a hypothesis for the number of segments can be provided to improve the results. We propose some methods to estimate the probability of the number of segments based on the transcription of the turn. The new labelling model includes the estimation of the probability of the number of segments in the turn. We tested this new approach with two different dialogue corpora: SwitchBoard and Dihana. The results show that this inclusion significantly improves the labelling accuracy.