Prosody-based Topic Segmentation for Mandarin Broadcast News

Automatic topic segmentation, separation of a discourse stream into its constituent stories or topics, is a necessary preprocessing step for applications such as information retrieval, anaphora resolution, and summarization. While significant progress has been made in this area for text sources and for English audio sources, little work has been done in automatic, acoustic feature-based segmentation of other languages. In this paper, we focus on prosody-based topic segmentation of Mandarin Chinese. As a tone language, Mandarin presents special challenges for applicability of intonation-based techniques, since the pitch contour is also used to establish lexical identity. We demonstrate that intonational cues such as reduction in pitch and intensity at topic boundaries and increase in duration and pause still provide significant contrasts in Mandarin Chinese. We also build a decision tree classifier that, based only on word and local context prosodic information without reference to term similarity, cue phrase, or sentence-level information, achieves boundary classification accuracy of 89--95.8% on a large standard test set.