语音CARE:在各种语言和语音任务环境中进行认知查的动态多式模式建模
Hossein Azadmaleki1, Yasaman Haghbin1, Sina Rashidi1
1Columbia University Irving Medical Center, New York, NY, USA.
NPJ digital medicine
|November 17, 2025
概括
SpeechCARE使用语音分析检测认知障碍. 这种人工智能工具对早期阿尔茨海默氏症和相关痴呆症 (ADRD) 和轻度认知障碍 (MCI) 检测有希望.
科学领域:
- 计算语言学计算语言学
- 医疗保健中的人工智能
- 神经科学是一个神经科学.
背景情况:
- 认知障碍,包括阿尔茨海默病和相关痴呆症 (ADRD) 和轻度认知障碍 (MCI),构成了全球重大健康挑战.
- 早期检测对于有效的管理和干预至关重要.
- 语音分析提供了一种评估认知功能的非侵入性方法.
研究的目的:
- 开发和评估SpeechCARE,一种多式变压器管道,用于从语音录音中检测认知障碍.
- 将个人分为ADRD,MCI和健康对照类别.
- 提高认知障碍检测的准确性和可扩展性.
主要方法:
- 采用多模式变压管道 (SpeechCARE),将声学 (mHuBERT) 和语言 (mGTE) 嵌入与人口统计数据相结合.
- 整合了先进的预处理管道,包括基于LLM的音频异常检测,语音任务识别,降噪和转录.
- 采用了一种新的自适应接机制和一个专门的编码组件来进行时间模式分析.
主要成果:
- 在NIA PREPARE挑战数据集中,SpeechCARE在一个持久测试中获得了72.11%的F1平均得分.
- 该模型在英语,西班牙语和普通话中展示了强大的多语言通用性.
- 值优化改善了轻度认知障碍 (MCI) 的回忆,该模型获得了NIA的特别认可.
结论:
- SpeechCARE有效地检测出语言的认知障碍,补充了现有的生物标志物.
- 该管道显示了早期,可扩展和多语言检测认知衰退的潜力.
- 需要进一步的研究和公平性分析,特别是针对不同的语言群体.
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