# Deep Learning for Depression Recognition with Audiovisual Cues: A Review

arXiv: https://arxiv.org/abs/2106.00610 · HTML: https://mlsanity.com/paper/2106.00610/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2021.

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review](https://mlsanity.com/paper/2201.09130/) (2022)
- [LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild](https://mlsanity.com/paper/2407.00024/) (2024)
- [Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China](https://mlsanity.com/paper/2408.04026/) (2024)
- [Automatic Depression Detection via Learning and Fusing Features from Visual Cues](https://mlsanity.com/paper/2203.00304/) (2022)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Towards Interpretability in Audio and Visual Affective Machine Learning: A Review](https://mlsanity.com/paper/2306.08933/) (2023)
- [Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder](https://mlsanity.com/paper/2412.06147/) (2024)
- [Towards Stable Cross-Domain Depression Recognition under Missing Modalities](https://mlsanity.com/paper/2512.06447/) (2025)
- [Attention-Based Acoustic Feature Fusion Network for Depression Detection](https://mlsanity.com/paper/2308.12478/) (2023)
- [Conformal Depression Prediction](https://mlsanity.com/paper/2405.18723/) (2024)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Automated speech- and text-based classification of neuropsychiatric conditions in a multidiagnostic setting](https://mlsanity.com/paper/2301.06916/) (2023)
- [Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents](https://mlsanity.com/paper/2405.20072/) (2024)
- [Language-Agnostic Analysis of Speech Depression Detection](https://mlsanity.com/paper/2409.14769/) (2024)
- [Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education](https://mlsanity.com/paper/2505.23631/) (2025)
- [MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features](https://mlsanity.com/paper/2511.15675/) (2025)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)
- [EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning](https://mlsanity.com/paper/2604.16579/) (2026)
- [MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation](https://mlsanity.com/paper/2606.11197/) (2026)

## Abstract (arXiv)

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies.