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 standardSwitchboardtranscribedspeechparsing task for overall parsing accuracy and editedworddetection. 1

Citations