Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Using Symbolic Prominence to Help Design Feature Subsets for Topic Classification and Clustering of Natural Human-Human Conversations

Constantinos Boulis, Mari Ostendorf

University of Washington, USA

In this work, we use the output of a symbolic prominence classifier rather than acoustic cues of prominence, to improve the tasks of clustering and classification of spontaneous conversations to topics. In our experiments, we combine the output of a prominence classifier with lexical feature selection and combination methods to build improved feature subsets. Evaluated for the task of topic classification on a subset of Switchboard-I, the combination method offered a 11% relative reduction of classification error compared to using lexical-only feature selection methods; similar gains are reported for clustering.

Full Paper

Bibliographic reference.  Boulis, Constantinos / Ostendorf, Mari (2005): "Using symbolic prominence to help design feature subsets for topic classification and clustering of natural human-human conversations", In INTERSPEECH-2005, 425-428.