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Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations

Elif Ecem Ozkan Orcid Logo, Patrick G.T. Healey, Tom Gurion, Julian Hough, Lorenzo Jamone Orcid Logo

IEEE Transactions on Cognitive and Developmental Systems, Pages: 1 - 1

Swansea University Author: Julian Hough

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DOI (Published version): 10.1109/tcds.2023.3254808

Abstract

People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e....

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Published in: IEEE Transactions on Cognitive and Developmental Systems
ISSN: 2379-8920 2379-8939
Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64930
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first_indexed 2023-11-07T21:42:49Z
last_indexed 2023-11-07T21:42:49Z
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spelling v2 64930 2023-11-07 Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations 082d773ae261d2bbf49434dd2608ab40 Julian Hough Julian Hough true false 2023-11-07 SCS People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e. speech disfluences (filled pauses, truncated words and phrases, word substitutions or reformulations), and computational tools now exist that can automatically detect these verbal phenomena. However, face-to-face conversation also exploits rich non-verbal resources and previous research suggests that self-repairs are associated with distinct hand movement patterns. This paper extends those results by exploring head and hand movements of both speakers and listeners using two motion parameters: height (vertical position) and 3D velocity. The results show that speech sequences containing self-repairs are distinguishable from fluent ones: speakers raise their hands and head more (and move more rapidly) during self-repairs. We obtain these results by analysing data from a corpus of 13 unscripted dialogues, and we discuss how these findings could support the creation of improved cognitive artificial systems for natural human-machine and human-robot interaction. Journal Article IEEE Transactions on Cognitive and Developmental Systems 1 1 Institute of Electrical and Electronics Engineers (IEEE) 2379-8920 2379-8939 Maintenance engineering, Oral communication, Natural language processing, Task analysis, Magnetic heads, Speech recognition, Human-robot interaction 10 3 2023 2023-03-10 10.1109/tcds.2023.3254808 http://dx.doi.org/10.1109/tcds.2023.3254808 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 10.13039/100009149-School of Electronic Engineering and Computer Science (Grant Number: EP/L01632X/1). 10.13039/501100000266-Engineering and Physical Sciences Research Council (Grant Number: EP/R02572X/1 and EP/S00453X/1). 2023-12-04T17:20:09.9214022 2023-11-07T21:40:36.3688528 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Elif Ecem Ozkan 0000-0002-9036-2149 1 Patrick G.T. Healey 2 Tom Gurion 3 Julian Hough 4 Lorenzo Jamone 0000-0002-1521-6168 5
title Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
spellingShingle Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
Julian Hough
title_short Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
title_full Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
title_fullStr Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
title_full_unstemmed Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
title_sort Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
author_id_str_mv 082d773ae261d2bbf49434dd2608ab40
author_id_fullname_str_mv 082d773ae261d2bbf49434dd2608ab40_***_Julian Hough
author Julian Hough
author2 Elif Ecem Ozkan
Patrick G.T. Healey
Tom Gurion
Julian Hough
Lorenzo Jamone
format Journal article
container_title IEEE Transactions on Cognitive and Developmental Systems
container_start_page 1
publishDate 2023
institution Swansea University
issn 2379-8920
2379-8939
doi_str_mv 10.1109/tcds.2023.3254808
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
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
url http://dx.doi.org/10.1109/tcds.2023.3254808
document_store_str 0
active_str 0
description People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e. speech disfluences (filled pauses, truncated words and phrases, word substitutions or reformulations), and computational tools now exist that can automatically detect these verbal phenomena. However, face-to-face conversation also exploits rich non-verbal resources and previous research suggests that self-repairs are associated with distinct hand movement patterns. This paper extends those results by exploring head and hand movements of both speakers and listeners using two motion parameters: height (vertical position) and 3D velocity. The results show that speech sequences containing self-repairs are distinguishable from fluent ones: speakers raise their hands and head more (and move more rapidly) during self-repairs. We obtain these results by analysing data from a corpus of 13 unscripted dialogues, and we discuss how these findings could support the creation of improved cognitive artificial systems for natural human-machine and human-robot interaction.
published_date 2023-03-10T17:20:10Z
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score 11.035634

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