# Deep Neural Baselines for Computational Paralinguistics

## TL;DR

This paper introduces an end-to-end deep neural network approach for detecting sleepiness from spoken language, achieving comparable performance to state-of-the-art models without extensive feature engineering.

## Contribution

It presents a simple, transferable deep learning method that directly analyzes audio data for sleepiness detection in paralinguistic tasks.

## Key findings

- Performs similarly to state-of-the-art models
- Eliminates need for feature engineering
- Applicable to other audio classification tasks

## Abstract

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.