Automatic Decision Detection in Meeting Speech

Abstract. Decision making is an important aspect of meetings in organisational settings, and archives of meeting recordings constitute a valuable source of information about the decisions made. However, standard utilities such as playback and keyword search are not sufficient for locating decision points from meeting archives. In this paper, we present the AMI DecisionDetector, a system that automatically detects and highlights where the decision-related conversations are. In this paper, we apply the models developed in our previous work [1], which detects decision-related dialogue acts (DAs) from parts of the transcripts that have been manually annotated as extract-worthy, to the task of detecting decision-related DAs and topic segments directly from complete transcripts. Results show that we need to combine features extracted from multiple knowledge sources (e.g., lexical, prosodic, DA-related, and topical class) in order to yield the model with the highest precision. We have provide...

• Type: Conference
• Year: 2007
• Where: MLMI
• Authors: Pei-yun Hsueh, Johanna D. Moore
• Keywords: AMI DecisionDetector, Decision Making, Machine Learning, Meeting, MLMI 2007