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Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions

Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals...

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Autores principales: Oertel, Catharine, Jonell, Patrik, Kontogiorgos, Dimosthenis, Mora, Kenneth Funes, Odobez, Jean-Marc, Gustafson, Joakim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280470/
https://www.ncbi.nlm.nih.gov/pubmed/34277714
http://dx.doi.org/10.3389/frobt.2021.555913
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author Oertel, Catharine
Jonell, Patrik
Kontogiorgos, Dimosthenis
Mora, Kenneth Funes
Odobez, Jean-Marc
Gustafson, Joakim
author_facet Oertel, Catharine
Jonell, Patrik
Kontogiorgos, Dimosthenis
Mora, Kenneth Funes
Odobez, Jean-Marc
Gustafson, Joakim
author_sort Oertel, Catharine
collection PubMed
description Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant’s perception of the robot, his behavior as well as the perception of third-party observers.
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spelling pubmed-82804702021-07-16 Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions Oertel, Catharine Jonell, Patrik Kontogiorgos, Dimosthenis Mora, Kenneth Funes Odobez, Jean-Marc Gustafson, Joakim Front Robot AI Robotics and AI Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant’s perception of the robot, his behavior as well as the perception of third-party observers. Frontiers Media S.A. 2021-07-01 /pmc/articles/PMC8280470/ /pubmed/34277714 http://dx.doi.org/10.3389/frobt.2021.555913 Text en Copyright © 2021 Oertel, Jonell, Kontogiorgos, Mora, Odobez and Gustafson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Oertel, Catharine
Jonell, Patrik
Kontogiorgos, Dimosthenis
Mora, Kenneth Funes
Odobez, Jean-Marc
Gustafson, Joakim
Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title_full Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title_fullStr Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title_full_unstemmed Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title_short Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
title_sort towards an engagement-aware attentive artificial listener for multi-party interactions
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280470/
https://www.ncbi.nlm.nih.gov/pubmed/34277714
http://dx.doi.org/10.3389/frobt.2021.555913
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