Cross-Genre Feature Comparisons for Spoken Sentence Segmentation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue International Journal of Semantic Computing Année : 2007

Cross-Genre Feature Comparisons for Spoken Sentence Segmentation

Résumé

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.
Fichier non déposé

Dates et versions

hal-00444099 , version 1 (05-01-2010)

Identifiants

  • HAL Id : hal-00444099 , version 1

Citer

Sebastien Cuendet, Dilek Hakkani-Tür, Elizabeth Shriberg, James Fung, Benoit Favre. Cross-Genre Feature Comparisons for Spoken Sentence Segmentation. International Journal of Semantic Computing, 2007, 1 (3), pp.335-346. ⟨hal-00444099⟩
14 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More