Integrated dialog act segmentation and classification using prosodic features and language models

This paper presents an integrated approach for the segmentation and classification of dialog acts (DA) in the Verbmobil project. In Verbmobil it is often sufficient to recognize the sequence of DAs occurring during a dialog between the two partners. In our previous work we segmented and classified a dialog in two steps: first we calculated hypotheses for the segment boundaries and decided for a boundary if the probabilities exceeded a predefined threshold level. Second we classified the segments into DAs using semantic classification trees or stochastic language models. In our new approach we integrate the segmentation and classification in the A*-algorithm to search for the optimal segmentation and classification of DAs on the basis of word hypotheses graphs (WHGs). The hypotheses for the segment boundaries are calculated with the help of a stochastic language model operating on the word chain and a multi-layer perceptron (MLP) classifying prosodic feature. The DA classification is done using a category based language model for each DA. For our experiments we used data from the Verbobil-corpus. (orig.)