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.