  Uncertainty Quantification (UQ) is an important building block for the
reliable use of neural networks in real-world scenarios, as it can be a useful
tool in identifying faulty predictions. Speech emotion recognition (SER) models
can suffer from particularly many sources of uncertainty, such as the ambiguity
of emotions, Out-of-Distribution (OOD) data or, in general, poor recording
conditions. Reliable UQ methods are thus of particular interest as in many SER
applications no prediction is better than a faulty prediction. While the
effects of label ambiguity on uncertainty are well documented in the
literature, we focus our work on an evaluation of UQ methods for SER under
common challenges in real-world application, such as corrupted signals, and the
absence of speech. We show that simple UQ methods can already give an
indication of the uncertainty of a prediction and that training with additional
OOD data can greatly improve the identification of such signals.
