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Báo cáo khoa học: "Targeted Help for Spoken Dialogue Systems: intelligent feedback improves naive users' performance"

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We present experimental evidence that providing naive users of a spoken dialogue system with immediate help messages related to their out-of-coverage utterances improves their success in using the system. A grammar-based 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.

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