Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.

翻译：痴呆和抑郁是老年人群中最常见的精神神经疾病，其重叠症状给鉴别诊断带来重大挑战。本研究探索开源权重的大语言模型，从154名德语受试者在标准化病史采集访谈中采集的语音样本中预测痴呆和抑郁严重程度。我们引入与既定整体性恶化量表对齐的观察者整体抑郁量表，实现情感和认知症状的并行整体分期。在两种设置中比较三种大语言模型（Mistral 3.1、DeepHermes、Qwen3）：(1) 零样本预测和(2) 基于大语言模型的特征提取用于支持向量回归，并采用人工转录和停顿增强转录。结果表明，大语言模型在零样本设置中有效预测抑郁严重程度（最佳平均绝对误差为0.60），而痴呆评估显著受益于结构化特征提取（最佳平均绝对误差为0.78），较零样本基线降低误差达35%。停顿增强转录达到与人工转录相当的竞争性能，验证了全自动筛查流程在精神神经鉴别评估中的可行性。