Integrating Multiple Knowledge Sources for Detection and Correction of Repairs in Human-Computer Dialog

INTEGRATING MULTIPLE KNOWLEDGE SOURCES FOR DETECTION AND CORRECTION OF REPAIRS IN HUMAN-COMPUTER DIALOG* John Bear, John Dowding, Elizabeth Shriberg t SRI International Menlo Park, California 94025 ABSTRACT We have analyzed 607 sentences of sponta- neous human-computer speech data containing re- pairs, drawn from a total corpus of 10,718 sen- tences. We present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correc- tion. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics. INTRODUCTION Spontaneous spoken language often includes speech that is not intended by the speaker to be part of the content of the utterance. This speech must be detected and deleted in order to correctly identify the intended meaning. The broad class of disfluencies encompasses a number of phenom- ena, including word fragments, interjections, filled pauses, restarts, and repairs. We are analyzing the repairs in a large subset (over ten thousand sentences) of spontaneous speech data collected for the DARPA Spoken Language Program3 We have categorized these disfluencies as to type and frequency, and are investigating methods for their automatic detection and correction. Here we re- port promising results on detection and correction of repairs by combining pattern matching, syn- tactic and semantic analysis, and acoustics. This paper extends work reported in an earlier paper *This research was supported by the Defense Advanced Research Projects Agency under Contract ONR N00014- 90-C-0085 with the Office of Naval Research. It was also supported by a Grant, NSF IRI-8905249, from the National Science Foundation. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official poll- cies, either expressed or implied, of the Defense Advanced Research Projects Agency of the U.S. Government, or of the National Science Foundation. tElizabeth Shriberg is also affiliated with the Depart- ment of Psychology at the University of California at Berkeley. 1DARPA is the Defense Advanced Research Projects Agency of the United States Government 56 (Shriberg et al., 1992a). The problem of disfluent speech for language understanding systems has been noted but has received limited attention. Hindle (1983) at- tempts to delimit and correct repairs in sponta- neous human-human dialog, based on transcripts containing an "edit signal," or external and reli- able marker at the "expunction point," or point of interruption. Carbonell and Hayes (1983) briefly describe recovery strategies for broken-off and restarted utterances in textual input. Ward (1991) addresses repairs in spontaneous speech, but does not attempt to identify or correct them. Our ap- proach is most similar to that of Hindle. It differs, however, in that we make no assumption about the existence of an explicit edit signal. As a reli- able edit signal has yet to be found, we take it as our problem to find the site of the repair automat- ically. It is the case, however, that cues to repair exist over a range of syllables. Research in speech pro- duction has shown …

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