Detecting anxiety from short clips of free-form speech
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Abstract (arXiv)
Barriers to accessing mental health assessments including cost and stigma continues to be an impediment in mental health diagnosis and treatment. Machine learning approaches based on speech samples could help in this direction. In this work, we develop machine learning solutions to diagnose anxiety disorders from audio journals of patients. We work on a novel anxiety dataset (provided through collaboration with Kintsugi Mindful Wellness Inc.) and experiment with several models of varying complexity utilizing audio, text and a combination of multiple modalities. We show that the multi-modal and audio embeddings based approaches achieve good performance in the task achieving an AUC ROC score of 0.68-0.69.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 23 Dec 2023.
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References · 14
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- 2019One Embedding To Do Them Allcited by 10
- 2023Classification for everyone : Building geography agnostic models for fairer recognitioncited by 5
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