Medizinische Physik and Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.
F1000Res. 2021 Apr 22;10:311. doi: 10.12688/f1000research.51784.1. eCollection 2021.
The effect of hearing impairment on speech perception was described by Plomp (1978) as a sum of a loss of class A, due to signal attenuation, and a loss of class D, due to signal distortion. While a loss of class A can be compensated by linear amplification, a loss of class D, which severely limits the benefit of hearing aids in noisy listening conditions, cannot. The hearing loss of class D is assumed to be the main reason why not few users of hearing aids keep complaining about the limited benefit of their devices in noisy environments. Working compensation strategies against it are unknown. Recently, in an approach to model human speech recognition by means of a re-purposed automatic speech recognition (ASR) system, the loss of class D was explained by introducing a level uncertainty which reduces the individual accuracy of spectro-temporal signal levels. Based on this finding, an implementation of a patented dynamic range manipulation scheme (PLATT) is proposed which aims to mitigate the effect of increased level uncertainty on speech recognition in noise by expanding spectral modulation patterns in the range of 2 to 4 ERB. This compensation approach is objectively evaluated regarding the benefit in speech recognition thresholds in noise using the ASR-based speech recognition model. Recommendations for an evaluation with human listeners are derived. The objective evaluation suggests that approximately half of the class D loss due to an increased level uncertainty might be compensable. To measure the effect with human listeners, an experiment needs to be carefully designed to prevent the confusion class A and D loss compensations. A working compensation strategy for the class D loss could provide previously unexploited potential for relief. Evidence has to be provided in experiments with human listeners.
听力障碍对语音感知的影响，Plomp（1978）描述为 A 类损失（由于信号衰减）和 D 类损失（由于信号失真）之和。虽然 A 类损失可以通过线性放大来补偿，但 D 类损失（严重限制了助听器在嘈杂聆听环境中的受益）却无法补偿。D 类听力损失被认为是助听器使用者对其设备在嘈杂环境中受益有限不断抱怨的主要原因。目前还不知道针对这种情况的工作补偿策略。最近，在通过重新利用自动语音识别（ASR）系统来模拟人类语音识别的方法中，通过引入降低个别声谱时变信号水平准确性的电平不确定性，解释了 D 类损失。基于这一发现，提出了一种专利的动态范围处理方案（PLATT）的实现，旨在通过扩展 2 到 4 ERB 范围内的光谱调制模式，减轻增加的电平不确定性对噪声中语音识别的影响。这种补偿方法通过基于 ASR 的语音识别模型，针对噪声中语音识别阈值的收益进行了客观评估。提出了用于人类听众评估的建议。客观评估表明，大约一半的由于电平不确定性增加而导致的 D 类损失可能是可补偿的。为了用人类听众进行测量，需要精心设计实验以防止 A 类和 D 类损失补偿的混淆。对于 D 类损失的工作补偿策略可以提供以前未被开发的缓解潜力。需要在人类听众的实验中提供证据。
