  We revisit the INTERSPEECH 2009 Emotion Challenge -- the first ever speech
emotion recognition (SER) challenge -- and evaluate a series of deep learning
models that are representative of the major advances in SER research in the
time since then. We start by training each model using a fixed set of
hyperparameters, and further fine-tune the best-performing models of that
initial setup with a grid search. Results are always reported on the official
test set with a separate validation set only used for early stopping. Most
models score below or close to the official baseline, while they marginally
outperform the original challenge winners after hyperparameter tuning. Our work
illustrates that, despite recent progress, FAU-AIBO remains a very challenging
benchmark. An interesting corollary is that newer methods do not consistently
outperform older ones, showing that progress towards `solving' SER is not
necessarily monotonic.
