耳蜗突触病变的低语境语音探针的模型引导设计

中文摘要 AI 辅助

耳蜗神经退化（CND）可损害超阈值编码而不提高纯音阈值，这使其在与毛细胞损失共存时的诊断变得复杂。我们提出了一种使用低语境元音-辅音-元音（VCV）音节来减少语言和语境线索的、用于CND检测的时间性和噪声性探针的统一比较。利用现象学听觉神经模型，我们模拟了在时间压缩、混响和噪声中语音条件下，21个VCV标记在不同呈现水平和七种CND特征下的响应。我们计算了内毛细胞电位与听觉神经神经图之间的互信息（MI），并量化了相对于正常听力基线的信息损失。时间压缩和调幅（AM）噪声产生了最大的模拟信息损失。然后，我们在一个辅音识别研究中评估了这些刺激，该研究涉及36名听力图正常的听众，其中12人报告在噪声中理解言语有困难。在安静环境中的40%时间压缩和单独的AM噪声都不能区分有和没有这些困难的听众。然而，在AM噪声中呈现的压缩语音将两组区分开来。模型预测与行为之间的这种部分一致性支持了我们基于MI的刺激设计框架，并推动了对用于CND检测的组合时间性和噪声性探针的进一步评估。

英文摘要

Cochlear neural degeneration (CND) can impair suprathreshold coding without elevating pure-tone thresholds, complicating its diagnosis when it coexists with hair cell loss. We present a unified comparison of temporal and noise-based probes for CND detection using low-context vowel-consonant-vowel (VCV) syllables to reduce linguistic and contextual cues. Using a phenomenological auditory nerve model, we simulated responses to 21 VCV tokens under time compression, reverberation, and speech-in-noise conditions across presentation levels and seven CND profiles. We computed mutual information (MI) between inner hair cell potentials and auditory nerve neurograms and quantified information loss relative to a normal-hearing baseline. Time compression and amplitude-modulated (AM) noise produced the largest modeled information losses. We then evaluated these stimuli in a consonant-identification study involving 36 listeners with normal audiograms, 12 of whom reported difficulty understanding speech in noise. Neither 40 percent time compression in quiet nor AM noise alone distinguished listeners with and without these difficulties. However, compressed speech presented in AM noise separated the two groups. This partial agreement between model predictions and behavior supports our MI-based stimulus design framework and motivates further evaluation of combined temporal and noise-based probes for CND detection.

发表机构

• Harvard University（哈佛大学）
• Eaton-Peabody Laboratories, Massachusetts Eye and Ear (MEEI)（伊顿-皮博迪实验室，马萨诸塞眼耳医院）
• National Acoustic Laboratories (NAL)（国家声学实验室）

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