Predicting Psychological Well-Being from Spontaneous Speech using LLMs
Authors: Erfan Loweimi, Sofia de la Fuente Garcia, Saturnino Luz
Summary
The authors use LLMs in a zero-shot setup to predict psychological well-being scores (Ryff PWB) from a few minutes of spontaneous speech recordings. They test 12 instruction-tuned models (Llama-3, Mistral, Gemma variants, etc.) using a domain-informed prompt co-designed with clinical psychology and linguistics experts. Best models achieve Spearman correlations up to 0.8, and the authors analyze prediction variability and use word clouds to identify which linguistic features drive predictions.
Main takeaways:
- LLMs can extract semantically meaningful psychological cues from spontaneous speech in zero-shot mode (no training on this task).
- Tested on 111 participants from the PsyVoiD database, using only a few minutes of audio transcripts.
- Best models reach Spearman correlation of 0.8 on 80% of the data for predicting well-being scores.
- The prompt was designed with domain experts in clinical psychology and linguistics.
- Statistical and keyword analyses provide some explainability for what linguistic features the models latch onto.
Relevance
Not directly connected to my persona/midtraining work. This is about extracting psychological signals from speech, while I'm investigating how behaviors get installed and conditioned in language models. No obvious bridge to conditional behavior or persona markers.
Threat model
Potential threat/caveat for clean result "Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas (LOW confidence)": this item discusses bias.
Abstract
arXiv:2605.11303v1 Announce Type: new Abstract: 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.