Word buffering models for improved speech repair parsing

Authors:
Tim Miller
Affiliations:
University of Minnesota -- Twin Cities
Venue:
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Year:
2009

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Abstract

This paper describes a time-series model for parsing transcribed speech containing disfluencies. This model differs from previous parsers in its explicit modeling of a buffer of recent words, which allows it to recognize repairs more easily due to the frequent overlap in words between errors and their repairs. The parser implementing this model is evaluated on the standard Switchboard transcribed speech parsing task for overall parsing accuracy and edited word detection.