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High-Frequency Sensorineural Hearing Loss Alters Cue-Weighting Strategies for Discriminating Stop Consonants in Noise.

Léo VarnetChloé LangletChristian LorenziDiane S LazardChristophe Micheyl
Published in: Trends in hearing (2020)
There is increasing evidence that hearing-impaired (HI) individuals do not use the same listening strategies as normal-hearing (NH) individuals, even when wearing optimally fitted hearing aids. In this perspective, better characterization of individual perceptual strategies is an important step toward designing more effective speech-processing algorithms. Here, we describe two complementary approaches for (a) revealing the acoustic cues used by a participant in a /d/-/g/ categorization task in noise and (b) measuring the relative contributions of these cues to decision. These two approaches involve natural speech recordings altered by the addition of a “bump noise.” The bumps were narrowband bursts of noise localized on the spectrotemporal locations of the acoustic cues, allowing the experimenter to manipulate the consonant percept. The cue-weighting strategies were estimated for three groups of participants: 17 NH listeners, 18 HI listeners with high-frequency loss, and 15 HI listeners with flat loss. HI participants were provided with individual frequency-dependent amplification to compensate for their hearing loss. Although all listeners relied more heavily on the high-frequency cue than on the low-frequency cue, an important variability was observed in the individual weights, mostly explained by differences in internal noise. Individuals with high-frequency loss relied slightly less heavily on the high-frequency cue relative to the low-frequency cue, compared with NH individuals, suggesting a possible influence of supra-threshold deficits on cue-weighting strategies. Altogether, these results suggest a need for individually tailored speech-in-noise processing in hearing aids, if more effective speech discriminability in noise is to be achieved.
Keyphrases
  • high frequency
  • hearing loss
  • transcranial magnetic stimulation
  • air pollution
  • machine learning
  • traumatic brain injury
  • deep learning
  • antiretroviral therapy
  • decision making