How much individualization is required to predict the individual effect of suprathreshold processing deficits? Assessing Plomp's distortion component with psychoacoustic detection thresholds and FADE

Abstract

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.