Due to the problems of occlusion effect, insufficient high-frequency gain, and acoustic feedback in conventional hearing aids, middle ear implants as a new type of hearing device have become an important compensatory tool for the treatment of patients with moderate to severe hearing loss. However, current speech intelligibility (SI) models are inadequate for predicting SI in hearing-impaired (HI) listeners after middle ear implants implantation. Moreover, the compensatory performance of middle ear implants before implantation remains unknown due to the invasive nature of the surgical procedure. Therefore, this study proposes a novel SI model that can predict the compensatory effects on SI after middle ear implants implantation. The model combines a physiologically nonlinear auditory preprocessing front-end with a short-term correlation analysis back-end. In normal-hearing (NH) listeners, the model accurately predicts speech reception thresholds (SRTs) and masking release under steady-state and fluctuating noise conditions. For HI listeners, the model modifies the parameters of outer hair cells and inner hair cells in the preprocessing front-end to simulate the patient’s audiogram, achieving excellent predictive capability for HI listeners’ test data. Overall, the proposed SI model can be used for the optimal design and algorithm fitting of middle ear implants tailored to patients with varying degrees of hearing loss, offering valuable insights for the clinical treatment of HI listeners.

中文翻译：

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使用人耳生理模型对听力受损听众的语音清晰度进行个性化预测

由于传统助听器存在遮挡效应、高频增益不足、声反馈等问题，中耳植入物作为一种新型助听器已成为治疗中重度听力损失患者的重要代偿工具。然而，当前的言语清晰度 (SI) 模型不足以预测听力受损 (HI) 听众在中耳植入物植入后的 SI。此外，由于手术过程的侵入性，中耳植入物在植入前的代偿性能仍然未知。因此，本研究提出了一种新的SI模型，可以预测中耳植入物植入后对SI的代偿效应。该模型结合了生理非线性听觉预处理前端和短期相关分析后端。对于听力正常 (NH) 的听众，该模型可以准确预测稳态和波动噪声条件下的语音接收阈值 (SRT) 和掩蔽释放。针对HI听者，模型通过修改预处理前端的外毛细胞和内毛细胞参数来模拟患者的听力图，对HI听者的测试数据实现优异的预测能力。总体而言，所提出的SI模型可用于针对不同程度听力损失患者的中耳植入物的优化设计和算法拟合，为HI听者的临床治疗提供有价值的见解。