kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Backchannel Behavior Influences the Perceived Personality of Human and Artificial Communication Partners
Tilburg University.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-8579-1790
Tilburg University.
2022 (English)In: Frontiers in Artificial Intelligence, E-ISSN 2624-8212, Vol. 5Article in journal (Refereed) Published
Abstract [en]

Different applications or contexts may require different settings for a conversational AI system, as it is clear that e.g., a child-oriented system would need a different interaction style than a warning system used in emergency situations. The current article focuses on the extent to which a system's usability may benefit from variation in the personality it displays. To this end, we investigate whether variation in personality is signaled by differences in specific audiovisual feedback behavior, with a specific focus on embodied conversational agents. This article reports about two rating experiments in which participants judged the personalities (i) of human beings and (ii) of embodied conversational agents, where we were specifically interested in the role of variability in audiovisual cues. Our results show that personality perceptions of both humans and artificial communication partners are indeed influenced by the type of feedback behavior used. This knowledge could inform developers of conversational AI on how to also include personality in their feedback behavior generation algorithms, which could enhance the perceived personality and in turn generate a stronger sense of presence for the human interlocutor.

Place, publisher, year, edition, pages
Frontiers Media SA , 2022. Vol. 5
National Category
Natural Language Processing
Identifiers
URN: urn:nbn:se:kth:diva-310438DOI: 10.3389/frai.2022.835298ISI: 000913759500001PubMedID: 35434608Scopus ID: 2-s2.0-85128478075OAI: oai:DiVA.org:kth-310438DiVA, id: diva2:1648514
Note

QC 20230215

Available from: 2022-03-31 Created: 2022-03-31 Last updated: 2025-02-07Bibliographically approved

Open Access in DiVA

fulltext(3286 kB)195 downloads
File information
File name FULLTEXT01.pdfFile size 3286 kBChecksum SHA-512
a937d7ede6cbb206dddedc938a372631f82ddc8c99b2d5445aebf7e5651033b0bcbec62e4d9f1239bc735ef5004a1a29df6771d3d068b8048433198ccdb51702
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Skantze, Gabriel

Search in DiVA

By author/editor
Skantze, Gabriel
By organisation
Speech, Music and Hearing, TMH
In the same journal
Frontiers in Artificial Intelligence
Natural Language Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 196 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 347 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf