Discriminatively Trained Gaussian Mixture Models for Sentence Boundary Detection

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