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使用机器学习自动开发语音噪音听力测试.

Sigrid Polspoel1,2, David R Moore3,4, De Wet Swanepoel5,6

  • 1Otolaryngology-Head and Neck Surgery, Section Ear and Hearing, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands.

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此摘要是机器生成的。

开发基于人工智能 (AI) 的语音噪音听力测试可以自动创建,降低成本并提高可访问性. 阿拉丁系统表现出高精度,为全球听力损失查提供了通用解决方案.

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阿拉丁是一个伟大的人.人工智能 (AI) 是一种人工智能.自动语音识别 (ASR) 是一种自动语音识别.数字在噪音中的测试合成语音是一种合成语音.文本转化为语音 (TTS) 服务

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科学领域:

  • 听力学 听力学是指听力学.
  • 人工智能的人工智能
  • 语音处理 语音处理

背景情况:

  • 听力损失对沟通有重大影响,尤其是在杂的环境中.
  • 传统的语音与噪音测试至关重要,但开发成本高,耗时长.
  • 这些测试的可访问性有限,特别是在资源较少的环境中.

研究的目的:

  • 引入一种基于人工智能的方法,用于自动化语音噪音听力测试的开发.
  • 为了降低创建高质量的听力测试所需的成本,时间和资源.
  • 为开发语言独立的听力测试制定通用准则.

主要方法:

  • 利用了文字转化为语音和自动语音识别 (ASR) 技术.
  • 开发了"阿拉丁"程序 (数字与噪音测试的自动语言独立开发).
  • 为数字在噪声 (DIN) 测试创建了合成语音材料,并使用ASR进行水平校正.

主要成果:

  • 阿拉丁测试显示了高的诊断准确性:特异性为84%,灵敏度为100%.
  • 性能与传统的参考DIN测试 (87%的特异性,100%的灵敏性) 相当.
  • 通过对正常听力和听力受损的参与者进行荷兰语和英语语言测试来验证该方法.

结论:

  • 阿拉丁方法为开发通用DIN听力测试提供了一种高效且具有成本效益的方法.
  • 这种人工智能驱动的创新显著提高了全球听力损失查和治疗的可访问性.
  • 为听力测试结果的跨语言比较提供了一个标准化的框架.