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Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition

Modern research on emotion recognition often deals with time-continuously labelled spontaneous interactions. Such data is much closer to real world problems in contrast to utterance-level categorical labelling in acted emotion corpora that have widely been used to date. While working with time-continuous labelling, one usually uses context-aware models, such as recurrent neural networks. The amount of context needed to show the best performance should be defined in this case. Despite of the research done in this field there is still no agreement on this issue. In this paper we model different amounts of contextual input data by varying two parameters: sparsing coefficient and time window size. A series of experiments conducted with different modalities and emotional labels on the RECOLA corpora has shown a strong pattern between the amount of context used in model and performance. The pattern remains the same for different pairs of modalities and label dimensions, but the intensity differs. Knowledge about an appropriate context can significantly reduce the complexity of the model and increase its flexibility.