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    <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.</abstract>
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%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

Markdown (Informal)

[Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition](https://aclanthology.org/2020.alta-1.13) (Keesing et al., ALTA 2020)

• Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition (Keesing et al., ALTA 2020)

ACL

• Aaron Keesing, Ian Watson, and Michael Witbrock. 2020. Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 104–109, Virtual Workshop. Australasian Language Technology Association.