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Predicting Psychological Well-Being from Spontaneous Speech using LLMs

arXiv 2605.11303 ↗First posted 11 May 2026cs.CLcited by 0

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Abstract (arXiv)

We investigate the use of Large Language Models (LLMs) for zero-shot prediction of Ryff Psychological Well-Being (PWB) scores from spontaneous speech. Using a few minutes of voice recordings from 111 participants in the PsyVoiD database, we evaluated 12 instruction-tuned LLMs, including Llama-3 (8B, 70B), Ministral, Mistral, Gemma-2-9B, Gemma-3 (1B, 4B, 27B), Phi-4, DeepSeek (Qwen and Llama), and QwQ-Preview. A domain-informed prompt was developed in collaboration with experts in clinical psychology and linguistics. Results show that LLMs can extract semantically meaningful cues from spontaneous speech, achieving Spearman correlations of up to 0.8 on 80% of the data. Additionally, to enhance explainability, we conducted statistical analyses to characterise prediction variability and systematic biases, alongside keyword-based word cloud analyses to highlight the linguistic features driving the models' predictions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 11 May 2026.

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References · 8

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  4. 2024Qwen2 Technical Reportcited by 2,120
  5. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  6. 2020Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic reviewcited by 29
  7. 2025Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Casecited by 3
  8. 2025Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledgecited by 3

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  3. 2024Recent Advancement of Emotion Cognition in Large Language Models
  4. 2025From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers
  5. 2025AIPsychoBench: Understanding the Psychometric Differences between LLMs and Humans
  6. 2022Predicting Human Psychometric Properties Using Computational Language Models
  7. 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
  8. 2025Navigating through the hidden embedding space: steering LLMs to improve mental health assessment