Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080-3021, USA.
Callier Center for Communication Disorders, University of Texas at Dallas, Richardson, TX 75080-3021, USA.
Sensors (Basel). 2022 Aug 12;22(16):6033. doi: 10.3390/s22166033.
Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting. The personalization was carried out based on the user preference model within the framework of maximum likelihood inverse reinforcement learning. The testing of ten subjects with hearing loss was conducted, which indicated that the personalized ADRO was preferred over the standard ADRO on average by about 10 times. Furthermore, a word recognition experiment was conducted, which showed that the personalized ADRO had no adverse impact on speech understanding as compared to the standard ADRO.
自适应动态范围优化(ADRO)是一种助听器适配理论,它通过使用一系列规则来调整多个频带的增益。这些规则反映了对声音水平的估计百分位发生次数与患有听力损失的人的可听度和舒适度听力水平的比较。在本文所报道的研究中,先前开发的机器学习方法被用于对 ADRO 适配进行个性化处理,以与标准 ADRO 助听器适配相比提供更好的听觉体验。个性化处理是基于最大似然逆强化学习框架中的用户偏好模型进行的。对十名听力损失受试者进行了测试,结果表明个性化 ADRO 平均比标准 ADRO 受偏好约 10 倍。此外,进行了一项单词识别实验,结果表明与标准 ADRO 相比,个性化 ADRO 对语音理解没有不利影响。