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一个跨语言的语音模型用于检测帕金森病

Wee Shin Lim¹, Shu-I Chiu², Pei-Ling Peng³

¹Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Journal of neural transmission (Vienna, Austria : 1996)

|December 31, 2024

概括

这项研究开发了一种使用语音分析的跨语言算法来检测帕金森病 (PD). 人工智能模型有效地将PD患者与不同语言和疾病阶段的健康个体区分开来.

科学领域:

计算语言学计算语言学
生物医学信号处理
神经学神经学

背景情况:

语音变化是帕金森病 (PD) 的关键指标.
评估不同语言的语音变异性对PD诊断构成了重大挑战.
现有的诊断方法可能无法充分捕捉不同语言背景下语音变化的细微差别.

研究的目的:

开发和验证一种跨语言算法,以区分帕金森病患者与健康对照.
通过使用台湾和韩国人口的语音数据来评估算法的有效性.
调查语音长度和特征提取对诊断性能的影响.

主要方法:

招募了347名PD患者和299名来自台湾和韩国的健康对照.
收集基于智能手机的语音录音,包括多种文本 (韩语) 和标准化文章 (台湾语).
在合并的数据集上使用机器学习分类器 (Random Forest,AdaBoost) 提取声学和语言特征.

主要成果:

随机森林分类器实现了早期PD的AUROC为0.82,高级PD的AUROC为0.90.
与单个语言模型 (韩国语0.87,台湾语0.88) 相比,跨语言模型显示出更高的性能 (AUROC 0.90).
较短的语音录音下降了表现 (早期PD的AUROC为0.72).

结论:

多方面的语音特征,包括声学和语言特征,对于跨语言的PD检测是有效的.
开发的算法显示了对非侵入性,可访问的帕金森病诊断的承诺.
需要对各种语音数据集进行进一步的研究,以完善诊断能力.

关键词:

生物标志物生物标志物深度学习模型深度学习模型面对面的面对面的面对面的面对面帕金森病是帕金森氏症的一种疾病.演讲演讲演讲

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