个体差异的综合建模 识别听力障碍成年人噪音中的言语
Robert A Lutfi1, Lindsey Kummerer1, Jungmee Lee1
1Auditory Behavioral Research Lab (ABRL), Department of Communication Sciences and Disorders, University of South Florida, Tampa, FL, USA.
Trends in hearing
|February 13, 2026
概括
感觉神经听力损失的个体经历了在噪音中识别言语的各种困难. 综合计算建模显示,灵敏度和决策噪声的差异显著影响语音识别性能.
科学领域:
- 听觉神经科学 听觉神经科学
- 计算机听力学 计算机听力学
- 语音感知 语音感知
背景情况:
- 难以在噪音中识别语音是感官神经听力损失的常见现象,但纯色调音频图无法完全解释个体差异.
- 听觉和非听觉因素都会导致语音识别的变化,以复杂的方式相互作用.
研究的目的:
- 开发和应用一个集成计算模型来识别语音识别中听众变异的来源.
- 在正常听力和听力受损群体中区分听者的表现差异.
主要方法:
- 使用了一种通用的计算模型,结合了同时的提示敏感性,提示依赖性和决策噪声.
- 该模型用于分析正常听力 (NH) 和听力受损 (HI) 听众的表现,处理语言,声学和统计线索,用于语音识别和噪音分离.
主要成果:
- 语音识别在不同组之间的差异主要与对静止声学线索的敏感性有关.
- 群体内部的差异主要是由与语音分离的可变统计线索相关的决策噪声驱动的.
- 听力受损的听众更依赖于他们最敏感的语音分离线索,并受益于将信息集中在那里.
结论:
- 综合建模为评估影响噪音语音识别的因素提供了一个可行的框架.
- 了解暗示灵敏度,依赖度和决策噪音对于解释听力障碍的个体差异至关重要.
- 计算模型可以帮助阐明在具有挑战性的听力条件下影响语音感知因素的复杂相互作用.
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