What an “ehm” leaks about you: mapping fillers into personality traits with quantum evolutionary feature selection algorithms

Tayarani, M., Esposito, A. and Vinciarelli, A. (2019) What an “ehm” leaks about you: mapping fillers into personality traits with quantum evolutionary feature selection algorithms. IEEE Transactions on Affective Computing, (doi:10.1109/TAFFC.2019.2930695) (Early Online Publication)

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Abstract

This work shows that fillers - short utterances like “ehm” and “uhm” - allow one to predict whether someone is above median along the Big-Five personality traits. The experiments have been performed over a corpus of 2,988 fillers uttered by 120 different speakers in spontaneous conversations. The results show that the prediction accuracies range between 74% and 82% depending on the particular trait. The proposed approach includes a feature selection step - based on Quantum Evolutionary Algorithms - that has been used to detect the personality markers, i.e., the subset of the features that better account for the prediction outcomes and, indirectly, for the personality of the speakers. The results show that only a relatively few features tend to be consistently selected, thus acting as reliable personality markers.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Tayarani, Dr Mohammad and Vinciarelli, Professor Alessandro
Authors: Tayarani, M., Esposito, A., and Vinciarelli, A.
College/School:College of Science and Engineering > School of Computing Science
Research Group:Social Signal Processing
Journal Name:IEEE Transactions on Affective Computing
Publisher:IEEE
ISSN:1949-3045
Published Online:23 July 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Transactions on Affective Computing 2019
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172889A Robot Training Buddy for adults with ASDAlessandro VinciarelliEngineering and Physical Sciences Research Council (EPSRC)EP/N035305/1Computing Science
171844SAM: Automated Attachment Analysis Using the School Attachment MonitorStephen BrewsterEngineering and Physical Sciences Research Council (EPSRC)EP/M025055/1Computing Science
303764EPSRC CDT - Socially Intelligent Artificial AgentsAlessandro VinciarelliEngineering and Physical Sciences Research Council (EPSRC)EP/S02266X/1Computing Science