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, 13(1), pp. 108-121. (doi: 10.1109/TAFFC.2019.2930695)

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: Published
• 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 13(1):108-121
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172889		A Robot Training Buddy for adults with ASD	Alessandro Vinciarelli	Engineering and Physical Sciences Research Council (EPSRC)	EP/N035305/1	Computing Science
171844		SAM: Automated Attachment Analysis Using the School Attachment Monitor	Stephen Brewster	Engineering and Physical Sciences Research Council (EPSRC)	EP/M025055/1	Computing Science
303764		EPSRC CDT - Socially Intelligent Artificial Agents	Alessandro Vinciarelli	Engineering and Physical Sciences Research Council (EPSRC)	EP/S02266X/1	Computing Science