most citedAn interpretable speech foundation model for depression detection by revealing prediction-relevant acoustic features from long speech

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Predicting Psychological Well-Being from Spontaneous Speech using LLMs

Erfan Loweimi, Sofia de la Fuente Garcia, Saturnino Luz

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…

cs.CL2026

Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness

Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi +2

LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability across three dimensions: intra…

cs.SD20263 cited

An interpretable speech foundation model for depression detection by revealing prediction-relevant acoustic features from long speech

Qingkun Deng, Saturnino Luz, Sofia de la Fuente Garcia

Speech-based depression detection tools could aid early screening. Here, we propose an interpretable speech foundation model approach to enhance the clinical applicability of such…

cs.SD2024

Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge

Fuxiang Tao, Bahman Mirheidari, Madhurananda Pahar +12

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To addres…

cs.CL2024

Connected Speech-Based Cognitive Assessment in Chinese and English

Saturnino Luz, Sofia De La Fuente Garcia, Fasih Haider +6

We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of sp…