  For speech emotion datasets, it has been difficult to acquire large
quantities of reliable data and acted emotions may be over the top compared to
less expressive emotions displayed in everyday life. Lately, larger datasets
with natural emotions have been created. Instead of ignoring smaller, acted
datasets, this study investigates whether information learnt from acted
emotions is useful for detecting natural emotions. Cross-corpus research has
mostly considered cross-lingual and even cross-age datasets, and difficulties
arise from different methods of annotating emotions causing a drop in
performance. To be consistent, four adult English datasets covering acted,
elicited and natural emotions are considered. A state-of-the-art model is
proposed to accurately investigate the degradation of performance. The system
involves a bi-directional LSTM with an attention mechanism to classify emotions
across datasets. Experiments study the effects of training models in a
cross-corpus and multi-domain fashion and results show the transfer of
information is not successful. Out-of-domain models, followed by adapting to
the missing dataset, and domain adversarial training (DAT) are shown to be more
suitable to generalising to emotions across datasets. This shows positive
information transfer from acted datasets to those with more natural emotions
and the benefits from training on different corpora.
