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The occurrence of disfluencies in fully natural speech poses difficult challenges for spoken language understanding systems. For example, although self-repairs occur in about 10% of spontaneous utterances, they are often unmodeled in speech recognition systems. This is partly due to the fact that little is known about the extent to which cues in the speech signal may facilitate automatic repair processing. In this paper, acoustic and prosodic cues to self-repairs are identified, based on an analysis of a corpus taken from the ARPA Air Travel Information System database, and methods are proposed for exploiting these cues for repair detection, especially the task of modeling word fragments, and repair correction. The relative contributions of these speech-based cues, as well as other text-based repair cues, are examined in a statistical model of repair site detection that achieves a precision rate of 91% and recall of 86% on a prosodically labeled corpus of repair utterances.
Nakatani et al. (Tue,) studied this question.