  Speech emotion recognition is an important component of any human centered
system. But speech characteristics produced and perceived by a person can be
influenced by a multitude of reasons, both desirable such as emotion, and
undesirable such as noise. To train robust emotion recognition models, we need
a large, yet realistic data distribution, but emotion datasets are often small
and hence are augmented with noise. Often noise augmentation makes one
important assumption, that the prediction label should remain the same in
presence or absence of noise, which is true for automatic speech recognition
but not necessarily true for perception based tasks. In this paper we make
three novel contributions. We validate through crowdsourcing that the presence
of noise does change the annotation label and hence may alter the original
ground truth label. We then show how disregarding this knowledge and assuming
consistency in ground truth labels propagates to downstream evaluation of ML
models, both for performance evaluation and robustness testing. We end the
paper with a set of recommendations for noise augmentations in speech emotion
recognition datasets.
