Medizinische Physik and Cluster of Excellence Hearing4all, CvO Universität, Oldenburg, 26111, Oldenburg, Germany.
Medizinische Physik and Cluster of Excellence Hearing4all, CvO Universität, Oldenburg, 26111, Oldenburg, Germany.
Hear Res. 2022 Dec;426:108609. doi: 10.1016/j.heares.2022.108609. Epub 2022 Sep 20.
Plomp introduced an empirical separation of the increased speech recognition thresholds (SRT) in listeners with a sensorineural hearing loss into an Attenuation (A) component (which can be compensated by amplification) and a non-compensable Distortion (D) component. Previous own research backed up this notion by speech recognition models that derive their SRT prediction from the individual audiogram with or without a psychoacoustic measure of suprathreshold processing deficits. To determine the precision in separating the A and D component for the individual listener with various individual measures and individualized models, SRTs with 40 listeners with a variation in hearing impairment were obtained in quiet, stationary noise, and fluctuating noise (ICRA 5-250 and babble). Both the clinical audiogram and an adaptive, precise sweep audiogram were obtained as well as tone-in-noise detection thresholds at four frequencies to characterize the individual hearing impairment. For predicting the SRT, the FADE-model (which is based on machine learning) was used with either of the two audiogram procedures and optionally the individual tone-in-noise detection thresholds. The results indicate that the precisely measured swept tone audiogram allows for a more precise prediction of the individual SRT in comparison to the clinical audiogram (RMS error of 4.3 dB vs. 6.4 dB, respectively). While an estimation from the precise audiogram and FADE performed equally well in predicting the individual A and D component, the further refinement of including the tone-in-noise detection threshold with FADE led to a slight improvement of prediction accuracy (RMS error of 3.3 dB, 4.6 dB and 1.4 dB, for SRT, A and D component, respectively). Hence, applying FADE is advantageous for scientific purposes where a consistent modeling of different psychoacoustical effects in the same listener with a minimum amount of assumptions is desirable. For clinical purposes, however, a precisely measured audiogram and an estimation of the expected D component using a linear regression appears to be a satisfactory first step towards precision audiology.
Plomp 将感音神经性听力损失患者的言语识别阈移（SRT）增加分为衰减（A）分量（可通过放大补偿）和不可补偿失真（D）分量。先前的研究通过言语识别模型支持了这一观点，这些模型从个体听力图中得出 SRT 预测值，无论是否考虑了阈上处理缺陷的心理声学测量。为了确定使用各种个体测量值和个体化模型对个体听力损失患者的 A 和 D 分量进行精确分离的精度，在安静、固定噪声和波动噪声（ICRA 5-250 和 babble）中，对 40 名听力障碍程度不同的患者进行了 SRT 测试。获得了临床听力图和自适应精确扫描听力图，以及四个频率的噪声中纯音探测阈，以描述个体听力损失。为了预测 SRT，使用 FADE 模型（基于机器学习），并根据两种听力图程序和可选的个体噪声中纯音探测阈进行预测。结果表明，与临床听力图相比，精确测量的扫描纯音听力图可以更精确地预测个体 SRT（分别为 RMS 误差 4.3dB 和 6.4dB）。虽然精确听力图和 FADE 的估计在预测个体 A 和 D 分量方面表现相当，但在 FADE 中进一步细化包括噪声中纯音探测阈可略微提高预测精度（SRT、A 和 D 分量的 RMS 误差分别为 3.3dB、4.6dB 和 1.4dB）。因此，对于科学目的，FADE 是有利的，因为需要在同一听众中以最小的假设量一致地建模不同的心理声学效应。然而，对于临床目的，精确测量的听力图和使用线性回归估计预期的 D 分量似乎是迈向精准听力的令人满意的第一步。
