@inproceedings{keesing-etal-2020-convolutional,
    title = "Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition",
    author = "Keesing, Aaron  and
      Watson, Ian  and
      Witbrock, Michael",
    editor = "Kim, Maria  and
      Beck, Daniel  and
      Mistica, Meladel",
    booktitle = "Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.alta-1.13/",
    pages = "104--109",
    abstract = "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."
}