A speech-first model for repair detection and correction

Interpreting fully natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self-corrections, or REPAIRS, little is known about the extent to which cues in the speech signal may facilitate repair processing. We identify several cues based on acoustic and prosodic analysis of repairs in the DARPA Air Travel Information System database, and propose methods for exploiting these cues to detect and correct repairs.