# Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

arXiv: https://arxiv.org/abs/1711.11155 · HTML: https://mlsanity.com/paper/1711.11155/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ](https://mlsanity.com/paper/2407.19340/) (2024)
- [Cost-effective Models for Detecting Depression from Speech](https://mlsanity.com/paper/2302.09214/) (2023)

## Abstract (arXiv)

We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8). We proposed a multi modal fusion model that combines three different modalities: audio, video , and text features. By training over AVEC 2017 data set, our proposed model outperforms each single modality prediction model, and surpasses the data set baseline with ice margin.