Improvements to a Speech Repair Parser

Abstract

Parsing is a common task for speech-recognition systems, but many parsers ignore the possibility of speech errors and repairs, which are very common in conversational language. The goal of this thesis is to examine a parsing system that can handle these occurrences and improve its performance by incorporating systems that use linguistic knowledge about speech errors and repairs. The basis for this thesis is a system for incremental parsing. The thesis shows additions that can be made to that system to allow for detection of speech errors and repairs. That is shown to be an improvement on previous incremental systems. An extension to the system is introduced which incorporates ideas about human short term memory and its relationship to speech errors. The system is then tested with many different configurations. Finally, the thesis concludes with a summary and discussion of the various results and lays out possible avenues for future work.