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使用机器学习识别语音障碍:一个范围审查协议.

Rijul Gupta1, Dhanshree R Gunjawate2, Duy Duong Nguyen2

  • 1School of Electrical and Information Engineering, The University of Sydney Faculty of Engineering and Information Technologies, Sydney, New South Wales, Australia.

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PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 对检测语音障碍有希望,但可靠性问题阻碍了临床使用. 本次审查确定了阻碍在医疗保健环境中采用ML算法的因素.

关键词:
因子分析,统计学,统计学信息技术 信息技术 信息技术 信息技术一个全方位的语言学.统计学和研究方法.语音病理学 语音病理学远程医疗远程医疗

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科学领域:

  • 语音和听力科学 语言和听力科学
  • 生物医学工程 生物医学工程
  • 计算机科学 计算机科学

背景情况:

  • 机器学习 (ML) 算法在检测语音障碍方面表现出高精度.
  • ML有可能帮助临床医生进行语音障碍分析和治疗评估.
  • 尽管进行了研究,但目前还没有ML算法足够可靠,足以广泛临床应用.

研究的目的:

  • 确定阻碍临床使用ML算法用于语音障碍检测的关键问题.
  • 确定标准的音频任务,声学特征,处理算法和影响ML有效性的环境因素.

主要方法:

  • 从2013年到2023年,在七个主要数据库 (Web of Science,Scopus,Compendex,CINAHL,Medline,IEEE Explore,Embase) 进行了全面的文献搜索.
  • 在大学图书馆的帮助下,改进了搜索策略,用于特定数据库的语法.
  • 数据的选择,提取和合成遵循了"系统性审查的首选报告项目和范围审查的元分析扩展" (PRISMA-ScR) 准则.

主要成果:

  • 该部分将在审查完成后填写.

结论:

  • 这一范围审查将提供关于在临床语音障碍检测中应用ML的挑战和局限性的见解.
  • 这些发现将指导未来的研究,以开发更强大的和临床上可行的语音病理学ML解决方案.