Combining Multiple Knowledge Sources for Dialogue Segmentation in Multimedia Archives
Proceedings of 45th Annual Meeting of the Association for Computational Linguistics
Date
03/11/2010Author
Hsueh, Pei-Yun
Moore, Johanna D.
Metadata
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.