Deep Neural Baselines for Computational Paralinguistics

Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Mueller, Steffen Illium


Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-end deep learning approach to detect and classify patterns reflecting sleepiness in the human voice. Our approach is based solely on a moderately complex deep neural network architecture. It may be applied directly on the audio data without requiring any specific feature engineering, thus remaining transferable to other audio classification tasks. Nevertheless, our approach performs similar to state-of-the-art machine learning models.


 DOI: 10.21437/Interspeech.2019-2478

Cite as: Elsner, D., Langer, S., Ritz, F., Mueller, R., Illium, S. (2019) Deep Neural Baselines for Computational Paralinguistics. Proc. Interspeech 2019, 2388-2392, DOI: 10.21437/Interspeech.2019-2478.


@inproceedings{Elsner2019,
  author={Daniel Elsner and Stefan Langer and Fabian Ritz and Robert Mueller and Steffen Illium},
  title={{Deep Neural Baselines for Computational Paralinguistics}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={2388--2392},
  doi={10.21437/Interspeech.2019-2478},
  url={http://dx.doi.org/10.21437/Interspeech.2019-2478}
}