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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.
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Kim et al. (2010) studied this question.