[en] We applied state-space modelling technique to estimate the cognitive workload of a speech-in-noise (SIN) recall task, based on participants’ oculo-motor responses to speech signals. We estimated common latent attention levels in 15 time bins and observed temporal changes between pupillary dilations and saccade frequencies, given that the both conditions were independent. We also compared two speech type factors (natural vs. synthetic) and three levels of signal-to-noise (-1dB, -3dB, and -5dB) using the estimated parameter distribution. The comparison of experimental factors provided us with insights into differences in participants’ processing of spoken information during a SIN recall task.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
DUBIEL, Mateusz ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Nakayama, Minoru; Tokyo Institute of Technology
Wang, Xin; National Institute of Informatics - Tokyo, Japan
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Modelling Attention Levels with Ocular Responses in a Speech-in-Noise Recall Task
Date de publication/diffusion :
30 mai 2023
Nom de la manifestation :
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications
Date de la manifestation :
from 30-05-2023 to 02-06-2023
Manifestation à portée :
International
Titre de l'ouvrage principal :
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications
Maison d'édition :
Association for Computing Machinery, Tubingen, Allemagne
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