在噪音和反响中识别句子时建模语境处理,适用于有听力损失和没有听力损失的听众
Kristin Sprenger1,2, Thomas Brand1,2
1Medizinische Physik, Carl von Ossietzky Universität Oldenburg, Oldenburg 26111, Germany.
The Journal of the Acoustical Society of America
|February 13, 2025
概括
这项研究发现,只有一个模型准确地测量了听力受损的听众如何使用语音上下文. 在调制噪音中,没有发现正常听力和听力受损个体之间的上下文处理差异.
科学领域:
- 听觉神经科学 听觉神经科学
- 语音感知 语音感知
- 人类的听力 人类的听力
背景情况:
- 语境处理对于语音识别至关重要,特别是对于听力损失的人来说.
- 了解听力损失的听众如何利用上下文对于开发有效的助听器策略至关重要.
- 以前的模型在准确量化上下文处理差异方面存在局限性.
研究的目的:
- 评估三个模型来量化人类语音识别中的上下文处理.
- 测试听力受损的听众是否更多地依赖语音上下文来弥补听力输入减少.
- 调查文本处理在各种噪音类型和反响条件中如何不同.
主要方法:
- 向正常听力和听力受损的听众呈现日常和矩阵句子 (可预测性不同).
- 在安静,稳态噪音和调制噪音下测试的句子,具有清洁和反响的语音.
- 使用引导测试进行显著性测试,以比较模型性能.
主要成果:
- 在三个评估的上下文模型中,只有一个在正常听力和听力受损的听众之间显示出显著的差异.
- 在静止和调制噪声条件之间,在上下文处理中没有观察到显著的差异.
- 区分先验和事后的上下文是必要的,以解释在回声中的语音感知.
结论:
- 评估的模型在捕捉听力受损听众的上下文处理细微差别方面存在局限性.
- 对于这些听众来说,语音上下文补偿策略可能不会在静止噪音和调制噪音之间有显著差异.
- 需要进一步的研究来完善模型,以了解环境在不利条件下的语音识别中的作用.
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