mlsanityData to 14 Oct 2026

Detecting anxiety from short clips of free-form speech

arXiv 2312.15272 ↗First posted 23 Dec 2023cs.CL cs.CY cs.LGcited by 0

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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

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
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  9. 2020Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognitioncited by 38
  10. 2018Footwear Size Recommendation Systemcited by 10
  11. 2019One Embedding To Do Them Allcited by 10
  12. 2023Classification for everyone : Building geography agnostic models for fairer recognitioncited by 5
2 more
  1. 2023Shaping Political Discourse using multi-source News Summarizationcited by 5
  2. 2023Exploring Graph Based Approaches for Author Name Disambiguationcited by 4

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