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.
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Su, J., Kane, B., Luz, S. (2010). Automatic Meeting Participant Role Detection by Dialogue Patterns. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds) Development of Multimodal Interfaces: Active Listening and Synchrony. Lecture Notes in Computer Science, vol 5967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12397-9_27
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DOI: https://doi.org/10.1007/978-3-642-12397-9_27
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