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Predicting interruptions in dyadic spoken interactions
Abstract
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.
Figures
Markers Placement.
Markers Placement.
Keywords
Interruption | Prediction | Hidden Conditional Field | Dyadic Interaction
Authors
Publication Date
2010/03/14
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI
10.1109/icassp.2010.5494991
Publisher
IEEE