Date of Original Version
Abstract or Description
We introduce a simple taxonomy of meeting states and participant roles. Our goal is to automatically detect the state of a meeting and the role of each meeting participant and to do so concurrent with a meeting. We trained a decision tree classiﬁer that learns to detect these states and roles from simple speech–based features that are easy to compute automatically. This classiﬁer detects meeting states 18% absolute more accurately than a random classiﬁer, and detects participant roles 10% absolute more accurately than a majority classiﬁer. The results imply that simple, easy to compute features can be used for this purpose.
Proceedings of the 8th International Conference on Spoken Language Processing (Interspeech 2004 - ICSLP).