用状态空间模型自动分析认知测试语音，提升痴呆早期识别准确率
Recognizing Dementia from Neuropsychological Tests with State Space Models
- 基于状态空间模型构建新框架，内存与计算随序列长度线性增长
- 在超1000小时数据上训练，细粒度分类准确率比以往方法高21%
- 可与大语言模型融合，适合医疗智能诊断与长期认知追踪场景
早期发现痴呆对及时医疗干预和改善患者预后至关重要。神经心理学测试广泛用于认知评估，但传统依赖人工评分。自动痴呆分类（ADC）系统旨在直接从测试语音中推断认知衰退。我们提出Demenba，一种基于状态空间模型的新型ADC框架，其内存与计算复杂度随序列长度线性增长。在弗雷明汉心脏研究参与者超过1000小时的认知评估数据上训练，其中部分受试者经裁定确诊为痴呆。相比先前方法，本方法在细粒度痴呆分类上提升21%，且参数更少。进一步分析显示，该模型与大语言模型融合后性能进一步提升，为更透明、可扩展的痴呆评估工具铺平道路。代码已公开。
原文摘要 · Abstract (English)
Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have traditionally relied on manual scoring. Automatic dementia classification (ADC) systems aim to infer cognitive decline directly from speech recordings of such tests. We propose Demenba, a novel ADC framework based on state space models, which scale linearly in memory and computation with sequence length. Trained on over 1,000 hours of cognitive assessments administered to Framingham Heart Study participants, some of whom were diagnosed with dementia through adjudicated review, our method outperforms prior approaches in fine-grained dementia classification by 21\%, while using fewer parameters. We further analyze its scaling behavior and demonstrate that our model gains additional improvement when fused with large language models, paving the way for more transparent and scalable dementia assessment tools. Code: https://anonymous.4open.science/r/Demenba-0861
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