Towards Automated Analysis of Spoken Discourse Using Discourse Topology

Susann Luperfoy and David Duff

This paper describes our current efforts in empirical analysis of human-human dialogue interaction data. The methods we propose abstracts away from the linguistic content of a dialogue to analyze acoustic and interaction phenomena directly. The focus is on properties of the speech signal and on language-independent interaction behavior as opposed to information content of the utterances exchanged between dialogue participants. We are exploring machine learning techniques for ways to convert our algorithms to trainable or adaptable system components.

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