mlsanityData to 7 Oct 2026

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

arXiv 2202.08210 ↗First posted 15 Feb 2022eess.AS cs.AI cs.SDcited by 37

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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.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 15 Feb 2022.

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5 of 26 references.

References · 5

  1. 2018Deep contextualized word representationscited by 2,285
  2. 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
  3. 2017A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancementcited by 34
  4. 2018Topic Modeling Based Multi-modal Depression Detectioncited by 29
  5. 2018Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressionscited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 11; 2025: 15; 2026: 9.

37 papers.

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  1. 2024Large Language Models in Mental Health Care: a Scoping Reviewcited by 34
  2. 2023Self-supervised representations in speech-based depression detectioncited by 18
  3. 2024When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detectioncited by 16
  4. 2024DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviewscited by 14
  5. 2024HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detectioncited by 12
  6. 2025The First MPDD Challenge: Multimodal Personality-aware Depression Detectioncited by 5
  7. 2024Computational Analysis of Stress, Depression and Engagement in Mental Health: A Surveycited by 4
  8. 2024Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessmentscited by 4
  9. 2024Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Researchcited by 3
  10. 2025Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generationcited by 3
  11. 2025A Comprehensive Review of Datasets for Clinical Mental Health AI Systemscited by 3
  12. 2025Towards Stable Cross-Domain Depression Recognition under Missing Modalitiescited by 3
25 more
  1. 2023Attention-Based Acoustic Feature Fusion Network for Depression Detectioncited by 2
  2. 2025Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approachescited by 2
  3. 2025Hierarchical Self-Supervised Representation Learning for Depression Detection from Speechcited by 2
  4. 2025It Hears, It Sees too: Multi-Modal LLM for Depression Detection By Integrating Visual Understanding into Audio Language Modelscited by 2
  5. 2024CANAMRF: An Attention-Based Model for Multimodal Depression Detectioncited by 1
  6. 2024Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detectioncited by 1
  7. 2024Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Languagecited by 1
  8. 2025Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarizationcited by 1
  9. 2025Automatic Depression Assessment using Machine Learning: A Comprehensive Surveycited by 1
  10. 2025MDD-Net: Multimodal Depression Detection through Mutual Transformercited by 1
  11. 2025CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysiscited by 1
  12. 2025Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detectioncited by 1
  13. 2026When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviewscited by 1
  14. 2026MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videoscited by 1
  15. 2024Multi-aspect Depression Severity Assessment via Inductive Dialogue Systemcited by 0
  16. 2025Exploring Machine Learning and Language Models for Multimodal Depression Detectioncited by 0
  17. 2025Mitigating Gender Bias in Depression Detection via Counterfactual Inferencecited by 0
  18. 2025Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Studycited by 0
  19. 2026Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modelingcited by 0
  20. 2026OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimizationcited by 0
  21. 2026TAAC: A gate into Trustable Audio Affective Computingcited by 0
  22. 2026Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Carecited by 0
  23. 2026Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasetscited by 0
  24. 2026Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectivescited by 0
  25. 2026Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Trainingcited by 0