Targeted help for spoken dialogue systems: intelligent feedback improves naive users' performance

Authors:
Beth Ann Hockey;Oliver Lemon;Ellen Campana;Laura Hiatt;Gregory Aist;James Hieronymus;Alexander Gruenstein;John Dowding
Affiliations:
NASA Ames Research Center, Moffet Field, CA;University of Edinburgh, Edinburgh, UK;University of Rochester, Rochester, NY;Stanford University, Stanford, CA;NASA Ames Research Center, Moffet Field, CA;NASA Ames Research Center, Moffet Field, CA;BeVocal, Inc., Mountain View, CA;NASA Ames Research Center, Moffet Field, CA
Venue:
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Year:
2003

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Abstract

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.