%0 Conference Proceedings %T Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition %A Keesing, Aaron %A Watson, Ian %A Witbrock, Michael %Y Kim, Maria %Y Beck, Daniel %Y Mistica, Meladel %S Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association %D 2020 %8 December %I Australasian Language Technology Association %C Virtual Workshop %F keesing-etal-2020-convolutional %X We test four models proposed in the speech emotion recognition (SER) literature on 15 public and academic licensed datasets in speaker-independent cross-validation. Results indicate differences in the performance of the models which is partly dependent on the dataset and features used. We also show that a standard utterance-level feature set still performs competitively with neural models on some datasets. This work serves as a starting point for future model comparisons, in addition to open-sourcing the testing code. %U https://aclanthology.org/2020.alta-1.13 %P 104-109