mlsanityData to 14 Oct 2026

Gender Bias in Depression Detection Using Audio Features

arXiv 2010.15120 ↗First posted 28 Oct 2020cs.SD cs.LG eess.AScited by 21

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Abstract (arXiv)

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression. Datasets such as Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets may result in skewed classification performance. In this paper we examine gender bias in the DAIC-WOZ dataset. We show that gender biases in DAIC-WOZ can lead to an overreporting of performance. By different concepts from Fair Machine Learning, such as data re-distribution, and using raw audio features, we can mitigate against the harmful effects of bias.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 7; 2025: 8; 2026: 2.

21 papers.

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  1. 2022Multi-modal Depression Estimation based on Sub-attentional Fusioncited by 15
  2. 2024DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviewscited by 15
  3. 2024HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detectioncited by 12
  4. 2025U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detectioncited by 12
  5. 2024Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cuescited by 11
  6. 2024Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & Chinacited by 7
  7. 2024Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Predictioncited by 6
  8. 2022A knowledge-driven vowel-based approach of depression classification from speech using data augmentationcited by 5
  9. 2024Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Researchcited by 3
  10. 2025Investigating the Sensitivity of Pre-trained Audio Embeddings to Common Effectscited by 3
  11. 2025Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detectioncited by 3
  12. 2025Fair Uncertainty Quantification for Depression Predictioncited by 2
9 more
  1. 2025Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approachescited by 2
  2. 2022Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voicecited by 1
  3. 2023Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorderscited by 0
  4. 2024Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorderscited by 0
  5. 2025Class Unbiasing for Generalization in Medical Diagnosiscited by 0
  6. 2025Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseasescited by 0
  7. 2025Mitigating Gender Bias in Depression Detection via Counterfactual Inferencecited by 0
  8. 2026DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detectioncited by 0
  9. 2026"OK Aura, Be Fair With Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detectioncited by 0