Paper: Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives

ACL ID P07-1128
Title Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Automatic segmentation is important for making multimedia archives comprehensi- ble, and for developing downstream infor- mation retrieval and extraction modules. In this study, we explore approaches that can segment multiparty conversational speech by integrating various knowledge sources (e.g. , words, audio and video recordings, speaker intention and context). In particu- lar, we evaluate the performance of a Max- imum Entropy approach, and examine the effectiveness of multimodal features on the task of dialogue segmentation. We also pro- vide a quantitative account of the effect of using ASR transcription as opposed to hu- man transcripts.