Paper: A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech

ACL ID C08-1072
Title A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

This paper describes an incremental ap- proach to parsing transcribed spontaneous speech containing disfluencies with a Hier- archical Hidden Markov Model (HHMM). This model makes use of the right-corner transform, which has been shown to in- crease non-incremental parsing accuracy on transcribed spontaneous speech (Miller and Schuler, 2008), using trees trans- formed in this manner to train the HHMM parser. Not only do the representations used in this model align with structure in speech repairs, but as an HMM-like time- series model, it can be directly integrated into conventional speech recognition sys- tems run on continuous streams of audio. A system implementing this model is eval- uated on the standard task of parsing the Switchboard corpus, and achieves an im- provement over the st...