Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/104187
Title: Classifying multimodal turn management in Danish dyadic first encounters
Authors: Navarretta, Costanza
Paggio, Patrizia
Keywords: Multilingual communication
Corpora (Linguistics)
Computational linguistics
Machine learning
Linguistics -- Methodology
Human-computer interaction
Management
Issue Date: 2013
Publisher: Linköping University Electronic Press
Citation: Navarretta, C., & Paggio, P. (2013, May). Classifying multimodal turn management in Danish dyadic first encounters. Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013) (pp. 133-147).
Abstract: This paper deals with multimodal turn management in an annotated Danish corpus of video recorded dyadic conversations between young people who meet for the first time. Conversation participants indicate whether they wish to give, take or keep the turn through speech as well as body behaviours. In this study we present an analysis of turn management body behaviours as well as classification experiments run on the annotated data in order to investigate how far it is possible to distinguish between the different types of turn management expressed by body behaviours using their shape and the co-occurring speech expressions. Our study comprises body behaviours which have not been previously investigated with respect to turn management, so that it not only confirms preceding studies on turn management in English but also provides new insight on how speech and body behaviours are used together in communication. The classification experiments indicate that the shape annotations of all kinds of body behaviour together with information about the gesturer’s co-occurring speech are useful to classify turn management types, and that the various behaviours contribute to the expression of turn features in different ways. Thus, knowledge of the different cues used by speakers in face-to-face communication to signal different types of turn shift provides the basis for modelling turn management, which is in turn key to implement natural conversation flow in multimodal dialogue systems
URI: https://www.um.edu.mt/library/oar/handle/123456789/104187
ISBN: 9789175195896
ISSN: 16503740
Appears in Collections:Scholarly Works - InsLin

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