需要多少语音数据来跟踪阿尔茨海默病中的语言变化? 随机长度,5分钟和1分钟自发语音样本的比较
Ulla Petti1, Simon Baker1, Anna Korhonen1
1Language Technology Lab, University of Cambridge, Cambridge, UK.
Digital biomarkers
|November 29, 2023
概括
针对阿尔茨海默病 (AD) 的语音分析从特定样本长度中受益. 一个5分钟的语音样本可靠地追踪认知衰退,这表明更长的录音为AD生物标志物提供了有价值的数据.
科学领域:
- 神经学和计算语言学 神经学和计算语言学
- 神经退行性疾病的生物标志物
背景情况:
- 语音变化被认为是阿尔茨海默病 (AD) 认知能力下降的潜在生物标志物.
- 语音样本用于准确分析的最佳持续时间仍然是一个开放的问题,影响数据收集效率.
研究的目的:
- 调查语音样本长度对阿尔茨海默病纵向记录分析的影响.
- 为了比较随机长度,5分钟和1分钟的语音样本在捕捉疾病进展中的有效性.
主要方法:
- 分析了来自YouTube视频的17名公众人物 (110个自发言语样本,其中9人被诊断为阿尔茨海默病).
- 在三个样本长度中提取和分析了456个语言特征:原始随机,5分钟和1分钟.
- 基于文本长度的敏感性,可比性和随时间跟踪变化的能力来评估特征.
主要成果:
- 封闭式音频样本 (5分钟和1分钟) 与随机长度样本相比显示出优势.
- 大多数语言特征在不同的样本长度中都是可比的或高度相关的,尽管对一些样本长度有所影响.
- 5分钟的样本数据集在跟踪疾病演变方面表现出了最高的可靠性,表明了额外记录时间的值.
结论:
- 语音样本长度显著影响语言特征的提取和跟踪阿尔茨海默病的进展.
- 进一步的研究对于确定最佳样本长度和在AD研究中标准化语音分析方法至关重要.
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