Targeted Help for Spoken Dialogue Systems

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 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.

• Type: Conference
• Year: 2003
• Where: EACL
• Authors: Beth Ann Hockey, Oliver Lemon, Ellen Campana, Laura M. Hiatt, Gregory Aist, James Hieronymus, John Dowding, Alexander Gruenstein
• Keywords: EACL 2003, Grammar-based Recognizer, Natural Language Processing, SLM Recognizer, SLM Recognizer Hypothesis