Paper: Using Conditional Random Fields For Sentence Boundary Detection In Speech

ACL ID P05-1056
Title Using Conditional Random Fields For Sentence Boundary Detection In Speech
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Sentence boundary detection in speech is important for enriching speech recogni- tion output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hid- den Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a condi- tional random field (CRF) for this task and relate results with this model to our prior work. We evaluate across two cor- pora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition out- put. In general, our CRF model yields a lower error rate than the HMM and Max- ent models on the NIST sentence bound- ary detection task in speech, al...