Tracking Conversational Context for Machine Mediation of Human Discourse

Tony Jebara, Yuri Ivanov, Ali Rahimi, and Alex Pentland

We describe a system that tracks conversational context using speech recognition and topic modeling. Topics are described by computing the frequency of words for each class. We thus reliably detect, in real-time, the currently active topic of a group discussion involving several individuals. One application of this ’situational awareness’ is a computer that acts as a mediator of the group meeting, offering feedback and relevant questions to stimulate further conversation. It also provides a temporal analysis of the meeting’s evolution. We demonstrate this application and discuss other possible impacts of conversational situation awareness.


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