Deep Neural Baselines for Computational Paralinguistics
This work addresses sleepiness detection in computational paralinguistics, but it is incremental as it matches rather than surpasses existing methods.
The authors tackled the problem of detecting sleepiness from spoken language using an end-to-end deep learning approach, achieving performance similar to state-of-the-art models without requiring feature engineering.
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.