Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research
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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.
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
28 of 114 references.
References · 28
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16 more
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.