Detecting anxiety from short clips of free-form speech

Prabhat Agarwal, Akshat Jindal, Shreya Singh
12/23/2023
cs.CLcs.CYcs.LG

No official code found. Best implementation: prabhat1081/Anxiety-Detection-from-free-form-audio-journals (5 stars, MIT, last commit 2021-03-22).

Abstract

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.

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Code Implementations(1)

prabhat1081/Anxiety-Detection-from-free-form-audio-journalsCommunity100%
MIT

Repository for CS224S project: Detecting anxiety from short clips of free-form speech

51Jupyter NotebookMar 22, 20215 years agoMIT

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Cite this paper

@article{agarwal2023detecting,
  title  = {Detecting anxiety from short clips of free-form speech},
  author = {Prabhat Agarwal and Akshat Jindal and Shreya Singh},
  year   = {2023},
  eprint = {2312.15272},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.15272v1}
}

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