Cross-Genre Feature Comparisons for Spoken Sentence Segmentation

TitleCross-Genre Feature Comparisons for Spoken Sentence Segmentation
Publication TypeJournal Article
Year of Publication2007
AuthorsCuendet, S., Hakkani-Tür D., Shriberg E., Fung J., & Favre B.
Published inInternational Journal of Semantic Computing
Other Numbers3868

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.

Bibliographic Notes

International Journal of Semantic Computing, Volume 1, Issue 3, World Scientific, USA, pp. 335-346

Abbreviated Authors

S. Cuendet, D. Hakkani-Tur, E. Shriberg, J. Fung, and B. Favre

ICSI Research Group


ICSI Publication Type

Article in journal or magazine