  We present STIR (STrongly Incremental Repair detection), a system that
detects speech repairs and edit terms on transcripts incrementally with minimal
latency. STIR uses information-theoretic measures from n-gram models as its
principal decision features in a pipeline of classifiers detecting the
different stages of repairs. Results on the Switchboard disfluency tagged
corpus show utterance-final accuracy on a par with state-of-the-art incremental
repair detection methods, but with better incremental accuracy, faster
time-to-detection and less computational overhead. We evaluate its performance
using incremental metrics and propose new repair processing evaluation
standards.
