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在口吃声学分析:一个机器学习研究研究.

Francesco Asci1,2, Luca Marsili3, Antonio Suppa1,2

  • 1Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.

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|July 17, 2023
PubMed
概括
此摘要是机器生成的。

人工智能通过语音分析准确识别口吃,帮助客观诊断. 这种机器学习方法在语音任务中可靠,并且可以通过智能手机远程使用.

关键词:
声学分析 声学分析家庭环境 家庭环境机器学习就是机器学习.吃吃的 吃吃的 吃吃的远程医疗远程医疗

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科学领域:

  • 语音语言病理学 语言病理学
  • 生物医学工程 生物医学工程
  • 计算语言学计算语言学

背景情况:

  • 吃是一种影响言语流性的神经发育障碍,通常通过主观感知评估来诊断.
  • 目前的诊断方法依赖于临床尺度和感知评估,缺乏客观措施.
  • 声学分析为客观评估的人 (PWS) 的流利性障碍提供了一个有希望的途径.

研究的目的:

  • 使用人工智能 (AI),特别是支持矢量机器 (SVM) 分类器,客观地和自动地评估口吃.
  • 研究的人群中与年龄相关的声音变化 (PWS).
  • 确定特定语音任务的有效性,以客观和自动地评估口吃.

主要方法:

  • 招募了53名PWS (20名儿童,33名成年人) 和71名年龄/性别匹配的对照.
  • 通过智能手机记录了自愿的元音发射和句子.
  • 使用SVM机器学习算法和人工神经网络 (ANN) 分析了音频样本,以求相关性.

主要成果:

  • 机器学习准确地区分了PWS和控制器 (88%的准确性).
  • 生理衰老显著影响了口吃,在分类儿童和成人方面准确度高 (92%).
  • 诊断准确度在不同的语音任务中是一致的,具有很强的临床-仪器相关性.

结论:

  • 人工智能驱动的声学分析 (SVM) 提供了客观,自动的口吃识别的可靠方法.
  • 高精度独立于语音任务,有助于临床诊断和管理.
  • 基于智能手机的音频收集可以实现潜在的远程医疗应用,用于在家庭环境中评估口吃.