website/_posts/research/2019-07-05-deep-neural-baselines.md

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layout	title	categories	excerpt	header
single	Deep-Neural Baseline	research	Introduction a deep baseline for audio classification.	teaser assets/figures/3_deep_neural_baselines_teaser.jpg

{:style="display:block; margin-left:auto; margin-right:auto; width:250px"} 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. {% cite elsner2019deep %}