An efficient statistical speech act type tagging system for speech translation systems

Abstract

This paper describes a new efficient speech act type tagging system. This system covers the tasks of (1) segmenting a turn into the optimal number of speech act units (SA units), and (2) assigning a speech act type tag (SA tag) to each SA unit. Our method is based on a theoretically clear statistical model that integrates linguistic, acoustic and situational information. We report tagging experiments on Japanese and English dialogue corpora manually labeled with SA tags. We then discuss the performance difference between the two languages. We also report on some translation experiments on positive response expressions using SA tags.