  Emotion recognition algorithms rely on data annotated with high quality
labels. However, emotion expression and perception are inherently subjective.
There is generally not a single annotation that can be unambiguously declared
"correct". As a result, annotations are colored by the manner in which they
were collected. In this paper, we conduct crowdsourcing experiments to
investigate this impact on both the annotations themselves and on the
performance of these algorithms. We focus on one critical question: the effect
of context. We present a new emotion dataset, Multimodal Stressed Emotion
(MuSE), and annotate the dataset using two conditions: randomized, in which
annotators are presented with clips in random order, and contextualized, in
which annotators are presented with clips in order. We find that contextual
labeling schemes result in annotations that are more similar to a speaker's own
self-reported labels and that labels generated from randomized schemes are most
easily predictable by automated systems.
