Authors:
Jonghwa Kim
and
Florian Lingenfelser
Affiliation:
University of Augsburg, Germany
Keyword(s):
Emotion recognition, Biosignal, Speech, Decision fusion, Multisensory data fusion, Pattern recognition, Affective computing, Human-computer interface.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
In this paper, we present a novel multi-ensemble technique for decision fusion of bimodal information. Exploiting the dichotomic property of 2D emotion model, various ensembles are built from given bimodal dataset containing multichannel physiological measures and speech. Through synergistic combination of the ensembles we investigated parametric schemes of decision-level fusion. Up to 18% of improved recognition accuracies are achieved compared to the results from unimodal classification.