Paper: Strongly Incremental Repair Detection

ACL ID D14-1009
Title Strongly Incremental Repair Detection
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We present STIR (STrongly Incremen- tal Repair detection), a system that de- tects speech repairs and edit terms on transcripts incrementally with minimal la- tency. STIR uses information-theoretic measures from n-gram models as its prin- cipal decision features in a pipeline of classifiers detecting the different stages of repairs. Results on the Switchboard dis- fluency tagged corpus show utterance-final accuracy on a par with state-of-the-art in- cremental repair detection methods, but with better incremental accuracy, faster time-to-detection and less computational overhead. We evaluate its performance us- ing incremental metrics and propose new repair processing evaluation standards.