https://github.com/EIHW/EmoNet. EmoNet provides an extensive command line interface which is comprehensively documented and can be used in a variety of multi-corpus transfer learning settings.
In this manuscript, the topic of multi-corpus Speech Emotion Recognition (SER) is approached from a deep transfer learning perspective. A large corpus of emotional speech data, EmoSet, is assembled from a number of existing SER corpora. In total, EmoSet contains 84181 audio recordings from 26 SER corpora with a total duration of over 65 hours. The corpus is then utilised to create a novel framework for multi-corpus speech emotion recognition, namely EmoNet. A combination of a deep ResNet architecture and residual adapters is transferred from the field of multi-domain visual recognition to multi-corpus SER on EmoSet. Compared against two suitable baselines and more traditional training and transfer settings for the ResNet, the residual adapter approach enables parameter efficient training of a multi-domain SER model on all 26 corpora. A shared model with only $3.5$ times the number of parameters of a model trained on a single database leads to increased performance for 21 of the 26 corpora in EmoSet. Measured by McNemar's test, these improvements are further significant for ten datasets at $p<0.05$ while there are just two corpora that see only significant decreases across the residual adapter transfer experiments. Finally, we make our EmoNet framework publicly available for users and developers at