Abstract
This paper investigates a scheme for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus based on hidden-event language models and a maximum entropy classifier for the modeling of word boundary types. Specifically, the modeling of the boundary types takes into account dependencies between the duration of a pause and its surrounding words. Results for the proposed method compare favorably with our previous work on the same task.
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