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 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.

• Type: Conference
• Year: 1993
• Where: ACL
• Authors: Christine H. Nakatani, Julia Hirschberg
• Keywords: ACL 1993, ACL 2007, Acoustic-prosodic Cues, Language Understanding Systems, Lexical Cues