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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Integrating cognitive and peripheral factors in predicting hearing-aid processing effectiveness.

James M Kates1, Kathryn H Arehart1, Pamela E Souza2

  • 1Department of Speech Language and Hearing Sciences, University of Colorado, Boulder, Colorado 80309.

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Summary

Individual factors like age and cognition impact hearing aid effectiveness. This study uses a neural network to predict speech intelligibility and quality, improving hearing aid processing for diverse listeners.

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Area of Science:

  • Audiology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Audiogram alone does not fully predict speech intelligibility or quality.
  • Hearing aid processing strategies, like frequency compression, aim to improve sound audibility.
  • Individual listener factors significantly influence speech perception outcomes.

Purpose of the Study:

  • To develop a neural network framework integrating multiple subject factors (age, cognition) for predicting speech intelligibility and quality.
  • To evaluate this framework for a nonlinear hearing aid processing strategy (frequency compression).
  • To create predictive models for listeners with normal hearing and mild-to-moderate high-frequency hearing loss.

Main Methods:

  • Development of a neural network framework incorporating individual subject factors.
  • Utilizing an ensemble averaging approach for neural network stability and to prevent overfitting.
  • Training and validating models on two distinct subject groups: normal hearing and mild-to-moderate sloping hearing loss.

Main Results:

  • The neural network framework successfully combined multiple subject factors to predict speech intelligibility and quality.
  • The models demonstrated predictive capabilities for both normal hearing and hearing-impaired listener groups.
  • The ensemble averaging approach mitigated overfitting issues in the neural network models.

Conclusions:

  • A comprehensive neural network model integrating individual factors can accurately predict speech intelligibility and quality.
  • This approach enhances the personalization of nonlinear hearing aid processing, specifically frequency compression.
  • Further research can refine these models for broader clinical application in audiology.