# DEPA: Self-Supervised Audio Embedding for Depression Detection

arXiv: https://arxiv.org/abs/1910.13028 · HTML: https://mlsanity.com/paper/1910.13028/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat](https://mlsanity.com/paper/2205.11764/) (2022)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [Automatic Depression Detection via Learning and Fusing Features from Visual Cues](https://mlsanity.com/paper/2203.00304/) (2022)
- [Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation](https://mlsanity.com/paper/2106.13393/) (2021)
- [Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study](https://mlsanity.com/paper/2404.04284/) (2024)
- [Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking](https://mlsanity.com/paper/2403.09717/) (2024)
- [Self-Supervised Embeddings for Detecting Individual Symptoms of Depression](https://mlsanity.com/paper/2406.17229/) (2024)
- [WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model](https://mlsanity.com/paper/2406.18731/) (2024)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)
- [Self-Supervised Versus Supervised Training for Segmentation of Organoid Images](https://mlsanity.com/paper/2311.11198/) (2023)
- [A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews](https://mlsanity.com/paper/2403.19441/) (2024)
- [Test-Time Training for Depression Detection](https://mlsanity.com/paper/2404.05071/) (2024)
- [RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis](https://mlsanity.com/paper/2506.07118/) (2025)
- [DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection](https://mlsanity.com/paper/2601.00303/) (2026)
- [MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation](https://mlsanity.com/paper/2606.11197/) (2026)

## Abstract (arXiv)

Depression detection research has increased over the last few decades, one major bottleneck of which is the limited data availability and representation learning. Recently, self-supervised learning has seen success in pretraining text embeddings and has been applied broadly on related tasks with sparse data, while pretrained audio embeddings based on self-supervised learning are rarely investigated. This paper proposes DEPA, a self-supervised, pretrained depression audio embedding method for depression detection. An encoder-decoder network is used to extract DEPA on in-domain depressed datasets (DAIC and MDD) and out-domain (Switchboard, Alzheimer's) datasets. With DEPA as the audio embedding extracted at response-level, a significant performance gain is achieved on downstream tasks, evaluated on both sparse datasets like DAIC and large major depression disorder dataset (MDD). This paper not only exhibits itself as a novel embedding extracting method capturing response-level representation for depression detection but more significantly, is an exploration of self-supervised learning in a specific task within audio processing.