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Using Simple Speech–Based Features to Detect the State of a Meeting and the Roles of the Meeting Participants
Proceedings of the 8th International Conference on Spoken Language Processing (Interspeech 2004 - ICSLP)
  • Satanjeev Banerjee, Carnegie Mellon University
  • Alexander I Rudnicky, Carnegie Mellon University
Date of Original Version
1-1-2004
Type
Conference Proceeding
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.

Citation Information
Satanjeev Banerjee and Alexander I Rudnicky. "Using Simple Speech–Based Features to Detect the State of a Meeting and the Roles of the Meeting Participants" Proceedings of the 8th International Conference on Spoken Language Processing (Interspeech 2004 - ICSLP) (2004)
Available at: http://works.bepress.com/alexander_rudnicky/74/