Automatic segmentation is important for\ud making multimedia archives comprehensible,\ud and for developing downstream information\ud retrieval and extraction modules. In\ud this study, we explore approaches that can\ud segment multiparty conversational speech\ud by integrating various knowledge sources\ud (e.g., words, audio and video recordings,\ud speaker intention and context). In particular,\ud we evaluate the performance of a Maximum\ud Entropy approach, and examine the\ud effectiveness of multimodal features on the\ud task of dialogue segmentation. We also provide\ud a quantitative account of the effect of\ud using ASR transcription as opposed to human\ud transcripts
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