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2016 | OriginalPaper | Chapter

Making Turn-Taking Decisions for an Active Listening Robot for Memory Training

Authors : Martin Johansson, Tatsuro Hori, Gabriel Skantze, Anja Höthker, Joakim Gustafson

Published in: Social Robotics

Publisher: Springer International Publishing

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Abstract

In this paper we present a dialogue system and response model that allows a robot to act as an active listener, encouraging users to tell the robot about their travel memories. The response model makes a combined decision about when to respond and what type of response to give, in order to elicit more elaborate descriptions from the user and avoid non-sequitur responses. The model was trained on human-robot dialogue data collected in a Wizard-of-Oz setting, and evaluated in a fully autonomous version of the same dialogue system. Compared to a baseline system, users perceived the dialogue system with the trained model to be a significantly better listener. The trained model also resulted in dialogues with significantly fewer mistakes, a larger proportion of user speech and fewer interruptions.

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Metadata
Title
Making Turn-Taking Decisions for an Active Listening Robot for Memory Training
Authors
Martin Johansson
Tatsuro Hori
Gabriel Skantze
Anja Höthker
Joakim Gustafson
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-47437-3_92

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