Segmenting conversations by topic, initiative, and style

Abstract

Topical segmentation is a basic tool for information access to audio records of meetings and other types of speech documents which may be fairly long and contain multiple topics. Standard segmentation algorithms are typically based on keywords, pitch contours or pauses. This work demonstrates that speaker initiative and style may be used as segmentation criteria as well. A probabilistic segmentation procedure is presented which allows the integration and modeling of these features in a clean framework with good results. Keyword based segmentation methods degrade significantly on our meeting database when speech recognizer transcripts are used instead of manual transcripts. Speaker initiative is an interesting feature since it delivers good segmentations and should be easy to obtain from the audio. Speech style variation at the beginning, middle and end of topics may also be exploited for topical segmentation and would not require the detection of rare keywords.