  When evaluating the performance of automatic speech recognition models,
usually word error rate within a certain dataset is used. Special care must be
taken in understanding the dataset in order to report realistic performance
numbers. We argue that many performance numbers reported probably underestimate
the expected error rate. We conduct experiments controlling for selection bias,
gender as well as overlap (between training and test data) in content, voices,
and recording conditions. We find that content overlap has the biggest impact,
but other factors like gender also play a role.
