通过大型语言模型和语音表示学习来增强痴呆症和认知衰退的检测
Karol Chlasta1,2, Piotr Struzik2, Grzegorz M Wójcik3
1Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Poland.
Frontiers in neuroinformatics
|December 22, 2025
概括
使用语音分析检测认知能力下降显示出早期痴呆症诊断的前景. 将GPT-4o等先进的人工智能模型与语音功能相结合,可以提高轻度认知障碍和痴呆症的预测准确度.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 痴呆症是一个重大的全球健康挑战.
- 早期发现认知衰退对于及时干预至关重要.
- 自发语音分析提供了一个非侵入性的诊断方法.
研究的目的:
- 为了预测语音样本的认知衰退,用于PROCESS信号处理大挑战 (ICASSP 2025).
- 评估将先进的人工智能模型与语音特征集成用于认知评估的有效性.
主要方法:
- 利用了eGeMAPS的 openSMILE,HuBERT (自我监督的语音表示) 和 GPT-4o (大型语言模型) 的功能.
- 集成功能与自定义的LSTM和ResMLP神经网络以及Scikit-learn回归器/分类器.
- 训练模型对157名参与者的语音数据进行了训练,并对40名参与者的单独测试组进行了评估.
主要成果:
- 轻GBM回归模型实现了2.7775的RMSE,在全球排名第10位.
- 在LSTM分类模型中,痴呆症/MCI/控制的F1得分为0.5521,全球排名第20位.
- 提出的方法在预测认知衰退方面超过了基线模型.
结论:
- 将大型语言模型与自我监督的语音表示集成,可以提高认知衰退的检测.
- 该方法为早期认知查提供了一个可扩展的,数据驱动的方法.
- 这项技术可能会支持神经心理信息学的未来应用.
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