| Paper: | SLP-P6.2 | ||
| Session: | Speech Understanding, Translation, Applications and Systems | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Speech Understanding | ||
| Title: | Discriminatively Trained Gaussian Mixture Models for Sentence Boundary Detection | ||
| Authors: | Marcus Tomalin, Phil C. Woodland, University of Cambridge, United Kingdom | ||
| Abstract: | This paper compares the performance of two types of Prosodic Feature Models (PFMs) in a sentence boundary detection task. Specifically, systems are compared that use discriminatively trained Gaussian Mixture Models (MMI-GMMs) and CART-Style Decision Trees (CDT-PFMs), along with task-specific language models, in a lattice-based decoding framework in order automatically to insert Slash Unit (SU) boundaries into Automatic Speech Recognition (ASR) transcriptions of input audio files. It is shown that a system which uses MMI-GMMs performs as well as a system that uses conventional CDT-PFMs. In addition, it is shown that, when the CDT-PFM and MMI-GMM systems are combined by taking weighted averages of their respective probability streams, Error rate improvements of up to 0.8% abs over the CDT-PFM baseline can be obtained for four different test sets. | ||
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