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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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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64930
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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. 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.
Keywords: Maintenance engineering, Oral communication, Natural language processing, Task analysis, Magnetic heads, Speech recognition, Human-robot interaction
College: Faculty of Science and Engineering
Funders: 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).
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