# Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research

arXiv: https://arxiv.org/abs/2406.04116 · HTML: https://mlsanity.com/paper/2406.04116/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 114 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [NLPositionality: Characterizing Design Biases of Datasets and Models](https://mlsanity.com/paper/2306.01943/) (2023)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Into the LAIONs Den: Investigating Hate in Multimodal Datasets](https://mlsanity.com/paper/2311.03449/) (2023)
- [Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers](https://mlsanity.com/paper/2108.02922/) (2021)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [Assessing Language Model Deployment with Risk Cards](https://mlsanity.com/paper/2303.18190/) (2023)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)
- [Gender Bias in Depression Detection Using Audio Features](https://mlsanity.com/paper/2010.15120/) (2020)
- [The Human Evaluation Datasheet 1.0: A Template for Recording Details of Human Evaluation Experiments in NLP](https://mlsanity.com/paper/2103.09710/) (2021)
- [Ethics Sheets for AI Tasks](https://mlsanity.com/paper/2107.01183/) (2021)
- [Reproducibility in NLP: What Have We Learned from the Checklist?](https://mlsanity.com/paper/2306.09562/) (2023)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Augmented Datasheets for Speech Datasets and Ethical Decision-Making](https://mlsanity.com/paper/2305.04672/) (2023)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [DEPAC: a Corpus for Depression and Anxiety Detection from Speech](https://mlsanity.com/paper/2306.12443/) (2023)
- [A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement](https://mlsanity.com/paper/2206.09530/) (2022)
- [Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews](https://mlsanity.com/paper/2008.03408/) (2020)
- [Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress](https://mlsanity.com/paper/2007.15815/) (2020)
- [Design Guidelines for Inclusive Speaker Verification Evaluation Datasets](https://mlsanity.com/paper/2204.02281/) (2022)
- [Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data](https://mlsanity.com/paper/2210.03303/) (2022)

## Built on by (most cited first, 3 of 3)

- [PsychiatryBench: A Multi-Task Benchmark for LLMs in Psychiatry](https://mlsanity.com/paper/2509.09711/) (2025)
- [TRI-DEP: A Trimodal Comparative Study for Depression Detection Using Speech, Text, and EEG](https://mlsanity.com/paper/2510.14922/) (2025)
- [Towards participatory speech dataset curation: A queer case study and conceptual framework](https://mlsanity.com/paper/2609.25496/) (2026)

## Abstract (arXiv)

Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research.