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.

翻译：隐性听力损失，或称耳蜗神经退行性病变（CND），会破坏阈上听觉编码而不影响临床检测阈值，因此难以诊断。本文提出一种信息论框架，通过量化内毛细胞（IHC）受体电位与听神经纤维（ANF）响应之间、以及声学输入与ANF响应之间的互信息（MI）损失，来评估最能揭示CND的言语刺激。我们采用现象学听觉模型，模拟了50个CVC词在纯净、时间压缩、混响及混合条件下、不同呈现声压级下的响应，并系统性地改变了低、中、高自发放电率纤维的存活比例。MI按通道计算于IHC与ANF响应之间，并跨特征频率进行积分。信息损失定义为相对于正常听力基线的损失。结果表明，随着CND加剧，MI呈现渐进性损失，其中时间压缩言语的损失最为显著，而混响产生的效应相对较小。这些发现表明，快速且时间密度高的言语是探测CND的最佳刺激，这为设计客观临床诊断方法提供了依据，同时也揭示了使用混响作为探测手段可能存在的问题。