No-Audio Multimodal Speech Detection in Crowded Social Settings task at MediaEval 2018

Conference paper (2018)

Authors

L.C. Cabrera Quiros Pattern Recognition and Bioinformatics - EEMCS , Instituto Tecnologico de Costa Rica
E. Gedik Pattern Recognition and Bioinformatics - EEMCS
H.S. Hung Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2018 L.C. Cabrera Quiros, E. Gedik, H.S. Hung
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

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.

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