The expressivity of turn-taking: understanding children pragmatics by hybrid classifiers

Segalin, C., Pesarin, A., Vinciarelli, A. and Cristani, M. (2013) The expressivity of turn-taking: understanding children pragmatics by hybrid classifiers. In: 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Paris, France, 3-5 Jul 2013, (doi: 10.1109/WIAMIS.2013.6616149)

93587.pdf - Accepted Version


Publisher's URL:


We analyze the effect of children age on pragmatic skills, i.e. on the way children manage the conversation dynamics. In particular, we focus exclusively on the turn-taking (who talks when and how much), reducing conversations as sequences of simple speech/silence periods. Employing a hybrid (generative + discriminative) classification framework, we demonstrate that such a simple signature is very informative, allowing to separate 22 ``pre-School'' conversations (between 3-4 years old children) and 24 ``School'' conversations (between 6-8 years old children), with 78% of accuracy. The framework exploits Steady Conversational Periods and Observed Influence Models as feature extractors, plus LASSO regression as feature selector and classifier. The generative nature of our method permits, as byproduct, to identify the pragmatic skills that better discriminate the two groups: notably, scholar children tend to have more frequent periods of sustained conversation, in a statistically significant way.

Item Type:Conference Proceedings
Additional Information:© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Segalin, C., Pesarin, A., Vinciarelli, A., and Cristani, M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Social Signal Processing
Copyright Holders:Copyright © 2013 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
543881SSPNet - Social Signal Processing Network.Alessandro VinciarelliEuropean Commission (EC)UNSPECIFIEDCOM - COMPUTING SCIENCE