INTERSPEECH 2004 - ICSLP
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.
Bibliographic reference. Banerjee, Satanjeev / Rudnicky, Alexander (2004): "Using simple speech-based features to detect the state of a meeting and the roles of the meeting participants", In INTERSPEECH-2004, 2189-2192.