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使用语音生物标志物进行脆弱性分类.

Yael Rosen-Lang1, Saad Zoubi2, Ron Cialic2

  • 1Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat-Gan, Israel.

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|July 22, 2023
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概括
此摘要是机器生成的。

语音分析可以预测老年人的脆弱性. 脆弱的个体表现出更不规则的语音模式,包括多样化的暂停长度和更高的音量波动,为人工智能驱动的健康评估铺平了道路.

关键词:
脆弱性 脆弱性 脆弱性机器学习是机器学习.演讲 演讲 演讲语音生物标志物语音生物标志物语音录制 语音录制 语音录制

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科学领域:

  • 老年学是一门学科.
  • 语音科学 语言科学
  • 生物医学工程 生物医学工程

背景情况:

  • 临床医生通过声音直观地评估患者的健康和脆弱性.
  • 作为健康指标的声音,与脆弱性相关的研究不足.
  • 客观的语音生物标志物可以提高脆弱性评估.

研究的目的:

  • 调查语音参数作为老年人虚弱的预测因素.
  • 为了识别特定的声学特征,使脆弱与不那么脆弱的个体有所区别.
  • 为基于人工智能的脆弱性检测工具奠定基础.

主要方法:

  • 从康复病房招募了53名70岁以上的参与者.
  • 使用洛克伍德脆弱指数评估脆弱性,将参与者分为最脆弱或不那么脆弱.
  • 录制参与者大声计数并分析语音生物标志物:音量,峰值/平均音量比,以及暂停特征.

主要成果:

  • 最脆弱的组显示出更高的峰值/平均体积比率 (p=0.03) 和更大的暂停长度变化 (p=0.002).
  • 最脆弱的参与者也有更长的总休息时间 (p=0.02).
  • 这些发现表明,在最脆弱的队列中,言语不规则性增加.

结论:

  • 在脆弱和不那么脆弱的老年人之间,言语特征有很大差异.
  • 语音生物标志物,特别是与语音不规则相关的生物标志物,显示出对脆弱性评估的希望.
  • 这项研究是开发人工智能工具的基本步骤,用于客观地识别脆弱性.