AI 中文总结
本研究对基于生物物理启发DNN的助听器算法进行主观评估，通过个性化耳蜗模型补偿外毛细胞损失，Matrix测试显示清晰度提升1%至27%，验证了其有效性。
AI 中文摘要
外毛细胞（OHC）损失是感音神经性听力损失（SNHL）的主要缺陷，会损害耳蜗放大和频率选择性，从而提高听力阈值。受生物物理启发的基于DNN的助听器（HA）算法已被提出用于补偿OHC缺陷，并在客观语音清晰度和质量指标（如HASPI、HASQI）方面显示出明显优势。然而，这些优势在人类听者中的全面主观验证仍然缺失。在本工作中，我们针对OHC缺陷提出了一种受生物物理启发的HA模型的主观评估。基于每位听者的纯音听力图，对听觉模型的耳蜗模块进行个性化处理，并将其集成到一个可训练系统中，该系统包括个性化模型和正常听力参考模型，随后使用Matrix测试对训练后的HA进行评估，比较未处理和HA处理的嘈杂语音的清晰度得分。结果显示，HA模型相对于未处理条件在+1%至+27%范围内具有显著优势，为该HA模型的有效性提供了行为学确认。本研究弥合了客观证据与感知证据之间的差距，为这一新一代基于DNN的HA算法铺平了道路，为其集成到下一代DNN加速芯片中用于可听设备和助听器奠定了基础。
英文摘要
Outer-hair-cell (OHC) loss is a primary deficit of sensorineural hearing loss (SNHL), impairing cochlear amplification and frequency selectivity and thereby elevating hearing thresholds. Biophysically-inspired DNN-based hearing-aid (HA) algorithms have been proposed to compensate for OHC deficits and have shown clear benefits in objective speech intelligibility and quality metrics (e.g. HASPI, HASQI). However, comprehensive subjective validation of these benefits in human listeners is still missing. In this work, we present a subjective evaluation of a biophysically-inspired HA model targeting OHC deficits. The cochlear module of an auditory model was individualized based on each listener's pure-tone audiogram and integrated into a trainable system, which includes the personalized model and a normal-hearing reference model, and the resulting trained HA was evaluated using a Matrix test comparing intelligibility scores for unprocessed and HA-processed noisy speech. The results revealed a significant benefit of the HA model over the unprocessed condition in the range of +1 to +27%, providing behavioral confirmation of the efficacy of the HA model. This study closes the gap between objective and perceptual evidence for this new generation of DNN-based HA algorithms, paving the way for their integration into next-generation DNN-accelerated chips for hearables and hearing aids.
