一个自动测量语音可理解性在多种语言和神经系统障碍的障碍症验证跨多种语言和神经系统障碍
Johannes Tröger1, Felix Dörr1, Louisa Schwed1
1ki elements GmbH, Saarbrücken, Germany.
Frontiers in digital health
|August 7, 2024
概括
一个新的数字工具,ki:SB-M理解度得分,可靠地测量了像帕金森病这样的运动语言障碍中的语音障碍. 这种自动化评估对临床试验和患者的生活质量有希望.
科学领域:
- 语音语言病理学 语音语言病理学
- 数字医学 数字医学
- 神经学 神经学
背景情况:
- 发音障碍 (dysarthria) 是一种运动语言障碍,显著影响语音理解能力和生活质量.
- 它在神经疾病中普遍存在,包括帕金森病 (PD),亨廷顿病 (HD),渐进性上核麻 (PSP) 和肌缩侧面硬化症 (ALS).
- 提高语音可理解性对患者至关重要,也是临床研究和药物开发的关键终点.
研究的目的:
- 为了验证ki:SB-M可理解性得分,这是语音可理解性的数字测量.
- 评估其可靠性和有效性在各种运动语音障碍和语言,坚持数字医学协会 (DiMe) V3框架.
主要方法:
- 利用了四个数据集:健康对照 (HCs) 和来自捷克,哥伦比亚和德国人口的PD,HD,PSP和ALS患者.
- 用自动语音识别 (ASR) 系统获得的ki:SB-M得分来评估语音可理解性.
- 验证包括ASR之间的可靠性,时间一致性,与黄金标准临床分数的相关性,以及HC和患者之间的组比较.
主要成果:
- 基:SB-M可理解度得分显示了良好的至优秀的评级者之间的可靠性和良好的至良的时间一致性.
- 在所有患者群体和语言中发现了SB-M得分和语音障碍的确立临床措施之间的显著相关性.
- 临床验证证实了病理组和健康对照组之间的可理解性得分存在显著差异,突出了其歧视能力.
结论:
- 基:SB-M理解度评分是可靠,有效和临床相关的工具,用于评估运动语言障碍的语音理解度.
- 这种自动化,客观和可扩展的评估有望改善临床试验.
- 未来的研究应该研究其在监测疾病进展和治疗疗效方面的应用.
相关概念视频
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