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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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多目标非侵入性助听器语音评估模型

Hsin-Tien Chiang1, Szu-Wei Fu2, Hsin-Min Wang3

  • 1Department of Electrical Engineering, The University of Texas at Dallas, Richardson, Texas 75080, USA.

The Journal of the Acoustical Society of America
|November 26, 2024
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这项研究介绍了HASA-Net+,这是用于助听器用户的高级语音评估模型. 它增强了在各种条件下对正常听力和听力受损的个体的语音质量和可理解性预测.

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科学领域:

  • 语音处理 语音处理
  • 助听器技术 助听器技术
  • 机器学习 机器学习

背景情况:

  • 由于现实应用中没有参考信号,参考自由言论评估至关重要.
  • 现有的深度学习模型显示出希望,但对听力受损 (HI) 主题的关注有限.
  • 非侵入性语音评估对于许多语音处理应用程序至关重要.

研究的目的:

  • 介绍HASA-Net+,一种改进的多目标,非侵入性的助听器语音评估模型.
  • 为了提高正常听力和HI听众的语音质量和可理解性预测.
  • 评估模型在各种声条件下的强度和概括能力.

主要方法:

  • HASA-Net+建立在之前的HASA-Net模型之上,结合了预先训练的语音基础模型和微调.
  • 该模型的预测能力被扩展到包括各种条件:杂的,无声的,反响的,脱响的和语音编码的语音.
  • 使用域外数据集验证了一般化.

主要成果:

  • HASA-Net+在预测语音质量和可理解性方面表现得更好.
  • 该模型在各种音响环境中证明了其稳健性,包括噪音和反响.
  • 使用域外数据集的验证证实了模型的概括能力.

结论:

  • HASA-Net+提供了一种强大而包容的解决方案,用于非侵入性语音质量和可理解性评估,特别是用于助听器用户.
  • 基础模型的整合和扩展的测试条件提高了它的适用性.
  • 该模型推进了用于助听器和各种声学场景的语音处理.