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Automatic recognition of social roles using long term role transitions in small group interactions
KTH, School of Electrical Engineering (EES), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2016 (English)In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2016, p. 2065-2069Conference paper, Published paper (Refereed)
Abstract [en]

Recognition of social roles in small group interactions is challenging because of the presence of disfluency in speech, frequent overlaps between speakers, short speaker turns and the need for reliable data annotation. In this work, we consider the problem of recognizing four roles, namely Gatekeeper, Protagonist, Neutral, and Supporter in small group interactions in AMI corpus. In general, Gatekeeper and Protagonist roles occur less frequently compared to Neutral, and Supporter. In this work, we exploit role transitions across segments in a meeting by incorporating role transition probabilities and formulating the role recognition as a decoding problem over the sequence of segments in an interaction. Experiments are performed in a five fold cross validation setup using acoustic, lexical and structural features with precision, recall and F-score as the performance metrics. The results reveal that precision averaged across all folds and different feature combinations improves in the case of Gatekeeper and Protagonist by 13.64% and 12.75% when the role transition information is used which in turn improves the F-score for Gatekeeper by 6.58% while the F-scores for the rest of the roles do not change significantly.

Place, publisher, year, edition, pages
2016. p. 2065-2069
Keywords [en]
Dynamic programming, Small group interaction, Social computing, Social roles, Reconfigurable hardware, Speech communication, Speech processing, Automatic recognition, Feature combination, Group interaction, Performance metrics, Structural feature, Transition probabilities, Speech recognition
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-202007DOI: 10.21437/Interspeech.2016-202ISI: 000409394401115Scopus ID: 2-s2.0-84994365873OAI: oai:DiVA.org:kth-202007DiVA, id: diva2:1077027
Conference
17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016, 8 September 2016 through 16 September 2016
Note

QC 20170224

Available from: 2017-02-24 Created: 2017-02-24 Last updated: 2018-01-13Bibliographically approved

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Chatterjee, Saikat

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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