美国国家老龄化研究所PREPARE挑战赛：基于语音的认知障碍早期检测——SpeechCARE解决方案
National Institute on Aging PREPARE Challenge: Early Detection of Cognitive Impairment Using Speech -- The SpeechCARE Solution
AI总结：
针对传统语音处理流水线在认知障碍早期检测中性能和泛化性有限的问题，提出多模态SpeechCARE流水线，结合预训练多语言Transformer模型和动态融合架构，在分类任务中取得良好效果并具备可解释性。
摘要：
阿尔茨海默病及相关痴呆（ADRD）影响60岁以上成年人的五分之一，但超过半数认知衰退患者未被诊断。基于语音的评估在早期检测中显示出潜力，因为语音运动规划缺陷会改变声学特征（如音高、语调），而记忆和语言障碍会导致句法和语义错误。然而，使用手工特征或通用音频分类器的传统语音处理流水线通常表现出有限的性能和泛化性。为解决这些局限性，我们引入SpeechCARE，这是一种多模态语音处理流水线，利用预训练的多语言声学和语言Transformer模型来捕捉与认知障碍相关的细微语音线索。受混合专家（MoE）范式启发，SpeechCARE采用动态融合架构，对基于Transformer的声学、语言和人口统计输入进行加权，允许整合额外模态（如社会因素、成像）并增强不同任务的鲁棒性。其稳健的预处理包括自动转录、基于大语言模型（LLM）的异常检测和任务识别。基于SHAP的可解释性模块和LLM推理突出了每种模态对决策的贡献。SpeechCARE在对认知健康者、轻度认知障碍（MCI）患者和阿尔茨海默病（AD）患者进行分类时，AUC=0.88，F1=0.72；在MCI检测中，AUC=0.90，F1=0.62。偏差分析显示差异极小，仅80岁以上成年人除外。缓解技术包括过采样和加权损失。未来工作包括在现实护理环境（如VNS Health、哥伦比亚ADRC）中部署，以及为纽约市代表性不足人群提供电子健康记录（EHR）集成的可解释性。
英文摘要：
Alzheimer's disease and related dementias (ADRD) affect one in five adults over 60, yet more than half of individuals with cognitive decline remain undiagnosed. Speech-based assessments show promise for early detection, as phonetic motor planning deficits alter acoustic features (e.g., pitch, tone), while memory and language impairments lead to syntactic and semantic errors. However, conventional speech-processing pipelines with hand-crafted features or general-purpose audio classifiers often exhibit limited performance and generalizability. To address these limitations, we introduce SpeechCARE, a multimodal speech processing pipeline that leverages pretrained, multilingual acoustic and linguistic transformer models to capture subtle speech-related cues associated with cognitive impairment. Inspired by the Mixture of Experts (MoE) paradigm, SpeechCARE employs a dynamic fusion architecture that weights transformer-based acoustic, linguistic, and demographic inputs, allowing integration of additional modalities (e.g., social factors, imaging) and enhancing robustness across diverse tasks. Its robust preprocessing includes automatic transcription, large language model (LLM)-based anomaly detection, and task identification. A SHAP-based explainability module and LLM reasoning highlight each modality's contribution to decision-making. SpeechCARE achieved AUC = 0.88 and F1 = 0.72 for classifying cognitively healthy, MCI, and AD individuals, with AUC = 0.90 and F1 = 0.62 for MCI detection. Bias analysis showed minimal disparities, except for adults over 80. Mitigation techniques included oversampling and weighted loss. Future work includes deployment in real-world care settings (e.g., VNS Health, Columbia ADRC) and EHR-integrated explainability for underrepresented populations in New York City.
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