Our goal is to automatically detect the functional roles that meeting participants play, as well as the expertise they bring to meetings. To perform this task, we build decision tree classiﬁers that use a combination of simple speech features (speech lengths and spoken keywords) extracted from the participants’ speech in meetings. We show that this algorithm results in a role detection accuracy of 83% on unseen test data, where the random baseline is 33.3%. We also introduce a simple aggregation mechanism that combines evidence of the participants’ expertise from multiple meetings. We show that this aggregation mechanism improves the role detection accuracy from 66.7% (when aggregating over a single meeting) to 83% (when aggregating over 5 meetings).
Available at: http://works.bepress.com/alexander_rudnicky/42/