基于人类耳朵的生理模型和层次性的尖端神经网络的语音可理解性预测
Yinxin Kou1, Houguang Liu1, Jie Wang2,3
1School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China.
The Journal of the Acoustical Society of America
|September 9, 2024
概括
本研究引入了一种新的语音可理解性 (SI) 预测模型,该模型结合了听觉处理和神经网络. 该模型准确地预测了语音感知,并模拟了中耳干预听力损失的效果.
科学领域:
- 听觉神经科学 听觉神经科学
- 计算式听觉神经科学 计算式听觉神经科学
- 生物医学工程 生物医学工程
背景情况:
- 语音可理解性 (SI) 对于沟通至关重要.
- 现有的SI模型往往缺乏对人类听觉系统的详细生理表征.
- 了解和预测SI对于诊断和治疗听力障碍至关重要.
研究的目的:
- 开发和验证一种新的语音可理解性 (SI) 预测模型.
- 将人类耳朵的先进生理细节纳入SI模型.
- 评估模型模拟听力损失干预措施的能力.
主要方法:
- 提出了一种具有听觉预处理的SI预测模型,一个层次性的尖端神经网络和基于相关性的决策处理.
- 听觉预处理组件模拟了生理细节,如移动波和耳非线性.
- 利用正常听力听众的数据和对耳硬化进行模拟的中耳干预的数据验证了该模型.
主要成果:
- 该模型准确地预测了各种噪音条件下的正常听力听众的语音可理解性数据.
- 该模型成功模拟了中耳干预对语音可理解性的影响.
- 在客观测量耳损伤和语音感知之间建立了基于模型的联系.
结论:
- 拟议的SI模型提供了准确的预测,并有效地模拟临床情景.
- 该模型是理解导电性听力损失和指导临床治疗的宝贵工具.
- 这种方法为术前评估和优化听觉刺激提供了一个强大的框架.
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