4 citations · 6 across the 6 of their papers we have counts for
6 papers
A Tutorial on Prompt Engineering: From Messy Thoughts to AI Workflows
Erfan Loweimi, Hadi Daneshvar, Samira Loveymi +4
This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice as a sequence of reusable de…
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…
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…
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…
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…
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…