# Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speech

arXiv: https://arxiv.org/abs/1911.00310 · HTML: https://mlsanity.com/paper/1911.00310/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Climate and Weather: Inspecting Depression Detection via Emotion Recognition](https://mlsanity.com/paper/2204.14099/) (2022)
- [Towards A Multi-agent System for Online Hate Speech Detection](https://mlsanity.com/paper/2105.01129/) (2021)
- [Detecting the Severity of Major Depressive Disorder from Speech: A Novel HARD-Training Methodology](https://mlsanity.com/paper/2206.01542/) (2022)

## Abstract (arXiv)

Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD). MDD describes the constant expression of certain emotions: negative emotions (low Valence) and lack of interest (low Arousal). High-performing intelligent systems would enhance MDD diagnosis in its early stages. In this paper, we present a new deep neural network architecture, called EmoAudioNet, for emotion and depression recognition from speech. Deep EmoAudioNet learns from the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Our model shows very promising results in predicting affect and depression. It works similarly or outperforms the state-of-the-art methods according to several evaluation metrics on RECOLA and on DAIC-WOZ datasets in predicting arousal, valence, and depression. Code of EmoAudioNet is publicly available on GitHub: https://github.com/AliceOTHMANI/EmoAudioNet