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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Cochlear implant speech intelligibility outcomes with structured and unstructured binary mask errors.

Abigail A Kressner1, Adam Westermann1, Jörg M Buchholz1

  • 1National Acoustic Laboratories, Australian Hearing, 16 University Avenue, Macquarie University, New South Wales 2109, Australia.

The Journal of the Acoustical Society of America
|March 4, 2016
PubMed
Summary
This summary is machine-generated.

Errors in estimated ideal binary masks (IBMs) impact speech intelligibility for cochlear implant (CI) recipients. Mask error structure significantly affects tolerance, with CI users generally less tolerant than normal-hearing listeners.

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Area of Science:

  • Auditory Neuroscience
  • Signal Processing
  • Speech Perception

Background:

  • Ideal binary masks (IBMs) enhance speech intelligibility for cochlear implant (CI) users.
  • Estimated masks in real-world scenarios contain errors, affecting performance.
  • Previous studies examined mask error effects in normal-hearing (NH) listeners, but not CI recipients.

Purpose of the Study:

  • To assess the impact of structured and unstructured binary mask errors on speech intelligibility in CI recipients.
  • To compare the tolerance of CI recipients to mask errors with that of NH listeners.
  • To investigate how error clustering and type influence tolerance.

Main Methods:

  • Systematic generation of binary masks with controlled errors using a statistical model.
  • Evaluation of speech intelligibility in CI recipients with these generated masks.
  • Analysis using mixed-effects models to compare CI and NH listener performance.

Main Results:

  • Clustering of mask errors significantly reduces tolerance.
  • Removing target-dominated regions is as detrimental as adding interferer-dominated regions.
  • CI recipients showed less tolerance to mask errors than NH listeners across most conditions.

Conclusions:

  • The structure of binary mask errors critically influences intelligibility and tolerance.
  • Error tolerance can change when different error types coexist.
  • Binary masking algorithm analysis must consider error structure, especially for CI users.