SEBASTIEN CUENDET
http://www.icsi.berkeley.edu International Computer Science Institute, Berkeley, CA 94704, USA
DILEK HAKKANI-TUR
http://www.icsi.berkeley.edu International Computer Science Institute, Berkeley, CA 94704, USA
ELIZABETH SHRIBERG
http://www.icsi.berkeley.edu International Computer Science Institute, Berkeley, CA 94704, USA
http://www.speech.sri.com SRI International, Menlo Park, CA 94025, USA
JAMES FUNG
http://www.icsi.berkeley.edu International Computer Science Institute, Berkeley, CA 94704, USA
BENOIT FAVRE
http://www.icsi.berkeley.edu International Computer Science Institute, Berkeley, CA 94704, USA

Automatic sentence segmentation of spoken language is an important precursor to downstream natural language processing. Previous studies combine lexical and prosodic features, but can impose significant computational challenges because of the large size of feature sets. Little is understood about which features most benefit performance, particularly for speech data from different speaking styles. We compare sentence segmentation for speech from broadcast news versus natural multi-party meetings, using identical lexical and prosodic feature sets across genres. Results based on boosting and forward selection for this task show that (1) features sets can be reduced with little or no loss in performance, and (2) the contribution of different feature types differs significantly by genre. We conclude that more efficient approaches to sentence segmentation and similar tasks can be achieved, especially if genre differences are taken into account.

Sentence segmentation; prosody; feature selection