Problem spotting in human-machine interaction

In human-human communication, dialogue participants are continuously sending and receiving signals on the status of the information 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 system would 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 correc tions, 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 dialogue systems, e.g., by providing quantitative criteria for changing the dialogue strategy or speech recognition engine.