Automatic dialog act segmentation and classification in multiparty meetings

We explore the two related tasks of dialog act (DA) segmentation and DA classification for speech from the ICSI Meeting Corpus. We employ simple lexical and prosodic knowledge sources, and compare results for human-transcribed versus automatically recognized words. Since there is little previous work on DA segmentation and classification in the meeting domain, our study provides baseline performance rates for both tasks. We introduce a range of metrics for use in evaluation, each of which measures different aspects of interest. Results show that both tasks are difficult, particularly for a fully automatic system. We find that a very simple prosodic model aids performance over lexical information alone, especially for segmentation. Both tasks, but particularly word-based segmentation, are degraded by word recognition errors. Finally, while classification results for meeting data show some similarities to previous results for telephone conversations, findings also suggest a potential difference with respect to the effect of modeling DA context.