Problem spotting in human-machine interaction

Emiel Krahmer, Marc Swerts, Mariet Theune, Mieke Weegels

    Research output: Contribution to conferencePaperpeer-review

    19 Citations (Scopus)
    61 Downloads (Pure)


    In human-human communication, dialogue participants are con-tinuously sending and receiving signals on the status of the inform-ation being exchanged. We claim that if spoken dialogue systems were able to detect such cues and change their strategy accordingly, the interaction between user and systemwould improve. Therefore, the goals of the present study are as follows: (i) to find out which positive and negative cues people actually use in human-machine interaction in response to explicit and implicit verification questions and (ii) to see which (combinations of) cues have the best predictive potential for spotting the presence or absence of problems. It was found that subjects systematically use negative/marked cues (more words, marked word order, more repetitions and corrections, less new information etc.) when there are communication problems. Using precision and recall matrices it was found that various combinations of cues are accurate problem spotters. This kind of information may turn out to be highly relevant for spoken dia-logue systems, e.g., by providing quantitative criteria for changing the dialogue strategy or speech recognition engine.
    Original languageUndefined
    Number of pages4
    Publication statusPublished - Sept 1999
    EventSixth European Conference on Speech Communication and Technology, EUROSPEECH 1999 - Budapest, Hungary
    Duration: 5 Sept 19999 Sept 1999


    ConferenceSixth European Conference on Speech Communication and Technology, EUROSPEECH 1999
    Other5-9 September 1999


    • IR-83423
    • EWI-22548

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