Background: Hearing-impaired (HI) individuals with similar ages and audiograms often demonstrate
substantial differences in speech-reception performance in noise. Traditional models
of speech intelligibility focus primarily on average performance for a given audiogram,
failing to account for differences between listeners with similar audiograms. Improved
prediction accuracy might be achieved by simulating differences in the distortion
that speech may undergo when processed through an impaired ear. Although some attempts
to model particular suprathreshold distortions can explain general speech-reception
deficits not accounted for by audibility limitations, little has been done to model
suprathreshold distortion and predict speech-reception performance for individual
HI listeners. Auditory-processing models incorporating individualized measures of
auditory distortion, along with audiometric thresholds, could provide a more complete
understanding of speech-reception deficits by HI individuals. A computational model
capable of predicting individual differences in speech-recognition performance would
be a valuable tool in the development and evaluation of hearing-aid signal-processing
algorithms for enhancing speech intelligibility.
Purpose: This study investigated whether biologically inspired models simulating peripheral
auditory processing for individual HI listeners produce more accurate predictions
of speech-recognition performance than audiogram-based models.
Research Design: Psychophysical data on spectral and temporal acuity were incorporated into individualized
auditory-processing models consisting of three stages: a peripheral stage, customized
to reflect individual audiograms and spectral and temporal acuity; a cortical stage,
which extracts spectral and temporal modulations relevant to speech; and an evaluation
stage, which predicts speech-recognition performance by comparing the modulation content
of clean and noisy speech. To investigate the impact of different aspects of peripheral
processing on speech predictions, individualized details (absolute thresholds, frequency
selectivity, spectrotemporal modulation [STM] sensitivity, compression) were incorporated
progressively, culminating in a model simulating level-dependent spectral resolution
and dynamic-range compression.
Study Sample: Psychophysical and speech-reception data from 11 HI and six normal-hearing listeners
were used to develop the models.
Data Collection and Analysis: Eleven individualized HI models were constructed and validated against psychophysical
measures of threshold, frequency resolution, compression, and STM sensitivity. Speech-intelligibility
predictions were compared with measured performance in stationary speech-shaped noise
at signal-to-noise ratios (SNRs) of −6, −3, 0, and 3 dB. Prediction accuracy for the
individualized HI models was compared to the traditional audibility-based Speech Intelligibility
Index (SII).
Results: Models incorporating individualized measures of STM sensitivity yielded significantly
more accurate within-SNR predictions than the SII. Additional individualized characteristics
(frequency selectivity, compression) improved the predictions only marginally. A nonlinear
model including individualized level-dependent cochlear-filter bandwidths, dynamic-range
compression, and STM sensitivity predicted performance more accurately than the SII
but was no more accurate than a simpler linear model. Predictions of speech-recognition
performance simultaneously across SNRs and individuals were also significantly better
for some of the auditory-processing models than for the SII.
Conclusions: A computational model simulating individualized suprathreshold auditory-processing
abilities produced more accurate speech-intelligibility predictions than the audibility-based
SII. Most of this advantage was realized by a linear model incorporating audiometric
and STM-sensitivity information. Although more consistent with known physiological
aspects of auditory processing, modeling level-dependent changes in frequency selectivity
and gain did not result in more accurate predictions of speech-reception performance.
Key Words
Auditory-processing model - cognitive processing - hearing impaired - sensorineural
hearing loss - spectral resolution - speech perception - suprathreshold distortion
- temporal resolution
