  Speech emotion recognition (SER) is an important aspect of effective
human-robot collaboration and received a lot of attention from the research
community. For example, many neural network-based architectures were proposed
recently and pushed the performance to a new level. However, the applicability
of such neural SER models trained only on in-domain data to noisy conditions is
currently under-researched. In this work, we evaluate the robustness of
state-of-the-art neural acoustic emotion recognition models in human-robot
interaction scenarios. We hypothesize that a robot's ego noise, room
conditions, and various acoustic events that can occur in a home environment
can significantly affect the performance of a model. We conduct several
experiments on the iCub robot platform and propose several novel ways to reduce
the gap between the model's performance during training and testing in
real-world conditions. Furthermore, we observe large improvements in the model
performance on the robot and demonstrate the necessity of introducing several
data augmentation techniques like overlaying background noise and loudness
variations to improve the robustness of the neural approaches.
