and Literature

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.