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听力状况的分类基于在句子感知过程中学生的测量

Patrycja Lebiecka-Johansen^1,2, Adriana A Zekveld¹, Dorothea Wendt^2,3

¹Department of Otolaryngology/Head & Neck Surgery, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Public Health Research Institute, the Netherlands.

Journal of speech, language, and hearing research : JSLHR

|February 14, 2025

概括

瞳孔膨胀的测量可以帮助区分正常听力和听力障碍的人的听力努力. 机器学习模型揭示了学生的反应如何根据听力状态,信号与噪声比和任务准确性而变化.

科学领域:

听觉神经科学听觉神经科学
人类的听觉感知
眼运动生理学眼运动生理学

背景情况:

在噪音中理解语音是具有挑战性的,特别是对于有听力障碍 (HI) 的人来说.
与正常听力 (NH) 同龄人相比,HI听众可能会改变听力努力分配.
瞳孔膨胀反应可能反映出这些努力差异.

研究的目的:

评估学生对与听力相关的倾听力度变化的敏感性.
调查如何学生测量区分NH和HI听众在噪音语音感知期间.
利用机器学习框架,根据学生对听力状态,信号噪声比 (SNR) 和任务响应的敏感性对学生的测量进行排名.

主要方法:

在自适应性语音接收值测试期间,从32个NH和32个HI听众收集了学生数据.
计算了各种瞳孔尺度,包括峰值瞳孔膨胀 (PPD),平均瞳孔膨胀 (MPD),主要瞳孔组件 (RPC) 和基线瞳孔大小 (BPS).
采用机器学习分类框架来评估使用学生措施预测听力状态,SNR和任务响应的能力.

主要成果:

为了准确地分类听力状态,SNR和任务响应,需要学生测量的组合.
既有措施,如PPD,RPC2和BPS,以及新的措施RPC1和RPC3,是关键预测因素.
分类表现因预测的因素和使用的特定学生指标而异.

结论:

机器学习可以有效地根据学生对影响噪音语音感知因素的敏感性对学生的测量进行排名.
学生的反应对听力状态,SNR和任务需求敏感,允许合理的分类.
这些因素对不同学生的测量有不同的影响,提高了我们对它们在听觉研究中的有用性的理解.

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