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This study investigates an application of nonlinear autoregressive (NAR) models to the prediction of the most likely time series of emotional state transitions of speakers engaged in dyadic conversations. While, previous methods analyzed each speaker in separation, the new approach proposes to couple both speakers into a nonlinear recursive predictive neural network system (NARX-NN). The NARX-NN system...
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