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CICLING
2006
Springer

Predicting Dialogue Acts from Prosodic Information

9 years 7 months ago
Predicting Dialogue Acts from Prosodic Information
In this paper, the influence of intonation to recognize dialogue acts from speech is assessed. Assessment is based on an empirical approach: manually tagged data from a spoken-dialogue and video corpus are used in a CARTstyle machine learning algorithm to produce a predictive model. Our approach involves two general stages: the tagging task, and the development of machine learning experiments. In the first stage, human annotators produce dialogue act taggings using a formal methodology, obtaining a highly enough tagging agreement, measured with Kappa statistics. In the second stage, tagging data are used to generate decision trees. Preliminary results show that intonation information is useful to recognize sentence mood, and sentence mood and utterance duration data contribute to recognize dialogue act. Precision, recall and Kappa values of the predictive model are promising. Our model can contribute to improve automatic speech recognition or dialogue management systems.
Sergio Rafael Coria Olguin, Luis Alberto Pineda Co
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CICLING
Authors Sergio Rafael Coria Olguin, Luis Alberto Pineda Cortés
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