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Prediction of Speech Recognition in Cochlear Implant Users by Adapting Auditory Models to Psychophysical Data

Abstract

Users of cochlear implants (CIs) vary widely in their ability to recognize speech in noisy conditions. There are many factors that may influence their performance. We have investigated to what degree it can be explained by the users' ability to discriminate spectral shapes. A speech recognition task has been simulated using both a simple and a complex models of CI hearing. The models were individualized by adapting their parameters to fit the results of a spectral discrimination test. The predicted speech recognition performance was compared to experimental results, and they were significantly correlated. The presented framework may be used to simulate the effects of changing the CI encoding strategy.

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Correspondence to Svante Stadler.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Stadler, S., Leijon, A. Prediction of Speech Recognition in Cochlear Implant Users by Adapting Auditory Models to Psychophysical Data. EURASIP J. Adv. Signal Process. 2009, 175243 (2009). https://doi.org/10.1155/2009/175243

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Keywords

  • Complex Model
  • Quantum Information
  • Speech Recognition
  • Recognition Task
  • Encode Strategy