Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives

Abstract

Automatic segmentation is important for making multimedia archives comprehensible, and for developing downstream information 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 particular, we evaluate the performance of a Maximum Entropy approach, and examine the effectiveness of multimodal features on the task of dialogue segmentation. We also provide a quantitative account of the effect of using ASR transcription as opposed to human transcripts.