Speech Perception with Spectrally Non-overlapping Maskers as Measure of Spectral Resolution in Cochlear Implant Users

Authors:
Publisher website
Google Scholar
Abstract
Poor spectral resolution contributes to the difficulties experienced by cochlear implant (CI) users when listening to speech in noise. However, correlations between measures of spectral resolution and speech perception in noise have not always been found to be robust. It may be that the relationship between spectral resolution and speech perception in noise becomes clearer in conditions where the speech and noise are not spectrally matched, so that improved spectral resolution can assist in separating the speech from the masker. To test this prediction, speech intelligibility was measured with noise or tone maskers that were presented either in the same spectral channels as the speech or in interleaved spectral channels. Spectral resolution was estimated via a spectral ripple discrimination task. Results from vocoder simulations in normal-hearing listeners showed increasing differences in speech intelligibility between spectrally overlapped and interleaved maskers as well as improved spectral ripple discrimination with increasing spectral resolution. However, no clear differences were observed in CI users between performance with spectrally interleaved and overlapped maskers, or between tone and noise maskers. The results suggest that spectral resolution in current CIs is too poor to take advantage of the spectral separation produced by spectrally interleaved speech and maskers. Overall, the spectrally interleaved and tonal maskers produce a much larger difference in performance between normal-hearing listeners and CI users than do traditional speech-in-noise measures, and thus provide a more sensitive test of speech perception abilities for current and future implantable devices.
Funding Information
  • National Institute on Deafness and Other Communication Disorders (DC012262)
  • Lions 5M International Hearing Foundation (N/A)

This publication has 44 references indexed in Scilit: