Gestural cue analysis in automated semantic miscommunication annotation

Masashi Inoue, Mitsunori Ogihara, Ryoko Hanada, Nobuhiro Furuyama

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting.

Original languageEnglish (US)
Pages (from-to)7-20
Number of pages14
JournalMultimedia Tools and Applications
Issue number1
StatePublished - Nov 2012


  • Face-to-face
  • Gesture
  • Psychotherapy
  • Semantic indexing

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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