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Ground truth labels obtained by averaging or majority voting are commonly used to train automatic emotion classifiers. However, ground truth labels fail to encapsulate interannotator variability and ignore the subjectivity of emotions. In this paper, we propose two viable approaches to model the subjectiveness of emotions by incorporating inter-annotator variability, which are soft labels and model ensembling, where each model represents an annotator. Using a deep neural network that recognizes emotions in real-time from one second windows of speech spectrograms, we demonstrate that both approaches lead to consistent improvement over using ground truth labels. It is empirically shown that the performance gain of the ensemble over the baseline model could be achieved using soft labels generated from multiple annotators.