Paper: A Speech-First Model For Repair Detection And Correction

ACL ID P93-1007
Title A Speech-First Model For Repair Detection And Correction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1993

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 RE- PAIRS, 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 a corpus of spontaneous speech, and propose methods for exploiting these cues to detect and correct repairs. We test our acoustic-prosodic cues with other lexical cues to repair identification and find that precision rates of 89-93% and recall of 78-83% can be achieved, depending upon the cues employed, from a prosodically labeled corpus.