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Conference Paper/Proceeding/Abstract 693 views 637 downloads

Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction

Philip R. Doyle, Justin Edwards, Leigh Clark Orcid Logo, Odile Dumbleton, Benjamin R. Cowan

Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, Pages: 1 - 12

Swansea University Author: Leigh Clark Orcid Logo

DOI (Published version): 10.1145/3338286.3340116

Abstract

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based...

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Published in: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services
ISBN: 9781450368254 978-1-4503-6825-4
Published: New York, NY, USA ACM 2019
URI: https://cronfa.swan.ac.uk/Record/cronfa52498
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first_indexed 2019-10-17T20:21:12Z
last_indexed 2023-02-23T04:04:29Z
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spelling 2023-02-22T15:30:34.2305005 v2 52498 2019-10-17 Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction 004ef41b90854a57a498549a462f13a0 0000-0002-9237-1057 Leigh Clark Leigh Clark true false 2019-10-17 SCS Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness. Conference Paper/Proceeding/Abstract Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services 1 12 ACM New York, NY, USA 9781450368254 978-1-4503-6825-4 speech interface; voice user interface; intelligent personal assistants; repertory grid; mental models; humanness. 1 10 2019 2019-10-01 10.1145/3338286.3340116 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2023-02-22T15:30:34.2305005 2019-10-17T14:34:33.1213674 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Philip R. Doyle 1 Justin Edwards 2 Leigh Clark 0000-0002-9237-1057 3 Odile Dumbleton 4 Benjamin R. Cowan 5 52498__15660__94c1dd988da54d4a981d65d79c71b695.pdf 1907.11585.pdf 2019-10-17T14:34:57.3230000 Output 218828 application/pdf Accepted Manuscript true 2019-10-17T00:00:00.0000000 true eng
title Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
spellingShingle Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
Leigh Clark
title_short Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
title_full Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
title_fullStr Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
title_full_unstemmed Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
title_sort Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
author_id_str_mv 004ef41b90854a57a498549a462f13a0
author_id_fullname_str_mv 004ef41b90854a57a498549a462f13a0_***_Leigh Clark
author Leigh Clark
author2 Philip R. Doyle
Justin Edwards
Leigh Clark
Odile Dumbleton
Benjamin R. Cowan
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services
container_start_page 1
publishDate 2019
institution Swansea University
isbn 9781450368254
978-1-4503-6825-4
doi_str_mv 10.1145/3338286.3340116
publisher ACM
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.
published_date 2019-10-01T04:04:54Z
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score 11.036706

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