用信息论方法识别听力损伤的敏感语音测试，提升早期诊断精度。
Utilizing Information Theoretic Approach to Study Cochlear Neural Degeneration
- 通过互信息量化听觉神经对语音刺激的编码损失
- 时间压缩语音下信息损失最显著，优于混响等干扰条件
- 为临床听力检测提供客观评估工具，适合听损早期筛查
隐性听力损失，即耳蜗神经退变（CND），会破坏阈上听觉编码但不影响常规听力阈值，难以诊断。本文提出一种信息论框架，通过计算内毛细胞受体电位与听觉神经纤维（ANF）响应、声输入与ANF响应间的互信息（MI）损失，评估能最大化揭示CND的语音刺激。采用现象学听觉模型，模拟在安静、时间压缩、混响及复合条件下50个辅音-元音-辅音词（CVC）在不同强度下的响应，系统性改变低、中、高自发率纤维的存活率。按特征频率通道计算互信息，并进行整合。信息损失以正常听力基线为参考。结果表明，随着CND加重，互信息逐渐下降，尤其在时间压缩语音下最为显著；而混响带来的影响相对较小。研究指出快速、时序密集的语音是探测CND的最佳工具，可指导客观临床诊断设计，并揭示混响作为探测手段的局限性。
原文摘要 · Abstract (English)
Hidden hearing loss, or cochlear neural degeneration (CND), disrupts suprathreshold auditory coding without affecting clinical thresholds, making it difficult to diagnose. We present an information-theoretic framework to evaluate speech stimuli that maximally reveal CND by quantifying mutual information (MI) loss between inner hair cell (IHC) receptor potentials and auditory nerve fiber (ANF) responses and acoustic input and ANF responses. Using a phenomenological auditory model, we simulated responses to 50 CVC words under clean, time-compressed, reverberant, and combined conditions across different presentation levels, with systematically varied survival of low-, medium-, and high-spontaneous-rate fibers. MI was computed channel-wise between IHC and ANF responses and integrated across characteristic frequencies. Information loss was defined relative to a normal-hearing baseline. Results demonstrate progressive MI loss with increasing CND, most pronounced for time-compressed speech, while reverberation produced comparatively smaller effects. These findings identify rapid, temporally dense speech as optimal probes for CND, informing the design of objective clinical diagnostics while revealing problems associated with reverberation as a probe.
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