Integrating cognitive and peripheral factors in predicting hearing-aid processing effectiveness

James M. Kates*, Kathryn H. Arehart, Pamela E. Souza

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Individual factors beyond the audiogram, such as age and cognitive abilities, can influence speech intelligibility and speech quality judgments. This paper develops a neural network framework for combining multiple subject factors into a single model that predicts speech intelligibility and quality for a nonlinear hearing-aid processing strategy. The nonlinear processing approach used in the paper is frequency compression, which is intended to improve the audibility of high-frequency speech sounds by shifting them to lower frequency regions where listeners with high-frequency loss have better hearing thresholds. An ensemble averaging approach is used for the neural network to avoid the problems associated with overfitting. Models are developed for two subject groups, one having nearly normal hearing and the other mild-to-moderate sloping losses.

Original languageEnglish (US)
Pages (from-to)4458-4469
Number of pages12
Journaljournal of the Acoustical Society of America
Volume134
Issue number6
DOIs
StatePublished - Dec 2013

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics

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