Automatic Detection and Correction of Repairs in Human-computer Dialog

Abstract

We have analyzed 607 sentences of spontaneous human-computer speech data containing repairs (drawn from a corpus of 10,718). We present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics.