Automatic meeting participant role detection by dialogue patterns

Abstract

We introduce a new concept of 'Vocalization Horizon' for automatic speaker role detection in general meeting recordings. We demonstrate that classification accuracy reaches 38.5% when Vocalization Horizon and other features (i.e. vocalization duration and start time) are available. With another type of Horizon, the Pause - Overlap Horizon, the classification accuracy reaches 39.5%. Pauses and overlaps are also useful vocalization features for meeting structure analysis. In our experiments, the Bayesian Network classifier outperforms other classifiers, and is proposed for similar applications. © 2010 Springer-Verlag.