No-Audio Multimodal Speech Detection in Crowded Social Settings task at MediaEval 2018
Laura Cabrera-Quiros (TU Delft - Pattern Recognition and Bioinformatics, Instituto Tecnologico de Costa Rica)
E. Gedik (TU Delft - Pattern Recognition and Bioinformatics)
H.S. Hung (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
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.