TY - JOUR
T1 - No-audio multimodal speech detection in crowded social setings task at MediaEval 2018
AU - Cabrera-Quiros, Laura
AU - Gedik, Ekin
AU - Hung, Hayley
N1 - Publisher Copyright:
© 2018 CEUR-WS. All rights reserved.
PY - 2018
Y1 - 2018
N2 - This overview paper provides a description of the automatic Human Behaviour Analysis (HBA) task for the MediaEval 2018. In its first edition, the HBA task focuses on analyzing one of the most basic elements of social behavior: the estimation of speaking status. Task participants are provided with cropped videos of individuals while interacting freely during a crowded mingle event that was captured by an overhead camera. Each individual is also wearing a badge-like device hung around the neck recording tri-axial acceleration. The goal of this task is to automatically estimate if a person is speaking or not using these two alternative modalities. In contrast to conventional speech detection approaches, no audio is used for this task. Instead, the automatic estimation system must exploit the natural human movements that accompany speech. The task seeks to achieve competitive estimation performance compared to audio-based systems by exploiting the multi-modal aspects of the problem. Copyright held by the owner/author(s).
AB - This overview paper provides a description of the automatic Human Behaviour Analysis (HBA) task for the MediaEval 2018. In its first edition, the HBA task focuses on analyzing one of the most basic elements of social behavior: the estimation of speaking status. Task participants are provided with cropped videos of individuals while interacting freely during a crowded mingle event that was captured by an overhead camera. Each individual is also wearing a badge-like device hung around the neck recording tri-axial acceleration. The goal of this task is to automatically estimate if a person is speaking or not using these two alternative modalities. In contrast to conventional speech detection approaches, no audio is used for this task. Instead, the automatic estimation system must exploit the natural human movements that accompany speech. The task seeks to achieve competitive estimation performance compared to audio-based systems by exploiting the multi-modal aspects of the problem. Copyright held by the owner/author(s).
UR - http://www.scopus.com/inward/record.url?scp=85059844841&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85059844841
SN - 1613-0073
VL - 2283
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018
Y2 - 29 October 2018 through 31 October 2018
ER -