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Targeted Help for Spoken Dialogue Systems: Intelligent Feedback Improves Naive Users' Performance

机译:口语对话系统的目标帮助:智能反馈提高了用户的天真表现

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We present experimental evidence that providing naive users of a spoken dia- logue system with immediate help messages related to their out-of-coverage utterances improves their success in using the system. A grammar-base recognizer and a Statistical Language Model (SLM) recognizer are run simultaneously. If the grammar-based recognizer suceeds, the less accurate SLM recognizer hypothesis is not used. When the grammar-based recognizer fails and the SLM recognizer produces a recognition hypothesis, this result is used by the Targeted Help agent to give the user feedback on what was recognized, a diagnosis of what was problematic about the utterance, and a related in-coverage example. The in-coverage example is intended to encourage alignment between user inputs and the language model of the system. We report on controlled experiments on a spoken dialogue system for command and control of a simulated robotic helicopter.

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