用大型语言模型和语音分析对认知障碍的多语言预测
1School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.
Brain sciences
|January 8, 2025
概括
早期发现认知障碍至关重要. 这项研究表明,使用人工智能模型进行特定语言的语音分析可以准确地评估英语和中文发言者的认知健康状况,从而提供了一个可扩展的解决方案.
科学领域:
- 语音处理 语音处理
- 人工智能的人工智能是人工智能.
- 神经科学是一个神经科学.
背景情况:
- 认知障碍是全球主要的健康问题,需要及早发现.
- 当前的诊断方法往往是主观的,昂贵的,难以获得的.
- 之前的语音分析研究在很大程度上忽视了多语言的背景.
研究的目的:
- 为了自动检测轻度认知障碍 (MCI) 和预测认知分数.
- 为了评估英语和中文发言者的表现.
- 探索语音基础模型对认知评估的有用性.
主要方法:
- 利用语音基础模型Whisper进行语言不可知语音嵌入提取.
- 采用集体模型来整合特定任务的信息.
- 与169名参与者一起参加了INTERSPEECH 2024 TAUKADIAL挑战赛.
主要成果:
- 在MCI分类中获得了81.83%的未加权平均回忆率 (第二名).
- 获得了1.196的根平均平方误差用于认知得分预测 (第1名).
- 突出了语言特异性的细微差别对纯粹的语言不可知论模型的重要性.
结论:
- 证明了使用Whisper嵌入的语言特定组合建模的有效性.
- 展示了一种可扩展的,非侵入性的认知健康评估方法.
- 在多语言认知障碍检测方面取得了最先进的结果,与阿尔茨海默病相关.
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