# Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model

arXiv: https://arxiv.org/abs/2202.08210 · HTML: https://mlsanity.com/paper/2202.08210/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions](https://mlsanity.com/paper/1811.08592/) (2018)

## Built on by (most cited first, 30 of 37)

- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [Self-supervised representations in speech-based depression detection](https://mlsanity.com/paper/2305.12263/) (2023)
- [When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection](https://mlsanity.com/paper/2402.13276/) (2024)
- [DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews](https://mlsanity.com/paper/2404.14463/) (2024)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [The First MPDD Challenge: Multimodal Personality-aware Depression Detection](https://mlsanity.com/paper/2505.10034/) (2025)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessments](https://mlsanity.com/paper/2405.11178/) (2024)
- [Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research](https://mlsanity.com/paper/2406.04116/) (2024)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)
- [A Comprehensive Review of Datasets for Clinical Mental Health AI Systems](https://mlsanity.com/paper/2508.09809/) (2025)
- [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)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches](https://mlsanity.com/paper/2509.25795/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)
- [Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection](https://mlsanity.com/paper/2511.12460/) (2025)
- [It Hears, It Sees too: Multi-Modal LLM for Depression Detection By Integrating Visual Understanding into Audio Language Models](https://mlsanity.com/paper/2511.19877/) (2025)
- [CANAMRF: An Attention-Based Model for Multimodal Depression Detection](https://mlsanity.com/paper/2401.02995/) (2024)
- [Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection](https://mlsanity.com/paper/2412.18614/) (2024)
- [Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language](https://mlsanity.com/paper/2501.00617/) (2024)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [MDD-Net: Multimodal Depression Detection through Mutual Transformer](https://mlsanity.com/paper/2508.08093/) (2025)
- [CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis](https://mlsanity.com/paper/2510.11233/) (2025)
- [OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization](https://mlsanity.com/paper/2602.10635/) (2026)
- [When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews](https://mlsanity.com/paper/2603.24651/) (2026)
- [MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos](https://mlsanity.com/paper/2607.27895/) (2026)
- [Multi-aspect Depression Severity Assessment via Inductive Dialogue System](https://mlsanity.com/paper/2410.21836/) (2024)
- [Exploring Machine Learning and Language Models for Multimodal Depression Detection](https://mlsanity.com/paper/2508.20805/) (2025)
- [Mitigating Gender Bias in Depression Detection via Counterfactual Inference](https://mlsanity.com/paper/2512.01834/) (2025)

## Abstract (arXiv)

Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.