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Speech emotion recognition is an important component of any human centeredsystem. But speech characteristics produced and perceived by a person can beinfluenced by a multitude of reasons, both desirable such as emotion, andundesirable such as noise. To train robust emotion recognition models, we needa large, yet realistic data distribution, but emotion datasets are often smalland hence are augmented with noise. Often noise augmentation makes oneimportant assumption, that the prediction label should remain the same inpresence or absence of noise, which is true for automatic speech recognitionbut not necessarily true for perception based tasks. In this paper we makethree novel contributions. We validate through crowdsourcing that the presenceof noise does change the annotation label and hence may alter the originalground truth label. We then show how disregarding this knowledge and assumingconsistency in ground truth labels propagates to downstream evaluation of MLmodels, both for performance evaluation and robustness testing. We end thepaper with a set of recommendations for noise augmentations in speech emotionrecognition datasets.

Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems