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ICASSP
2010
IEEE

Predicting interruptions in dyadic spoken interactions

13 years 11 months ago
Predicting interruptions in dyadic spoken interactions
Interruptions occur frequently in spontaneous conversations, and they are often associated with changes in the flow of conversation. Predicting interruption is essential in the design of natural human-machine spoken dialog interface. The modeling can bring insights into the dynamics of human-human conversation. This work utilizes Hidden Condition Random Field (HCRF) to predict occurrences of interruption in dyadic spoken interactions by modeling both speakers’ behaviors before a turn change takes place. Our prediction model, using both the foreground speaker’s acoustic cues and the listener’s gestural cues, achieves an F-measure of 0.54, accuracy of 70.68%, and unweighted accuracy of 66.05% on a multimodal database of dyadic interactions. The experimental results also show that listener’s behaviors provides an indication of his/her intention of interruption.
Chi-Chun Lee, Shrikanth Narayanan
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Chi-Chun Lee, Shrikanth Narayanan
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