Evaluation of an Articulation-Index Based Model for Predicting the Effects of Adaptive Frequency Response Hearing Aids The Articulation Index (AI) was used to evaluate an “adaptive frequency response” (AFR) hearing aid with amplification characteristics that automatically change to become more high-pass with increasing levels of background noise. Speech intelligibility ratings of connected discourse by normal-hearing subjects were predicted well by an empirically derived AI transfer function. ... Research Article
Research Article  |   December 01, 1990
Evaluation of an Articulation-Index Based Model for Predicting the Effects of Adaptive Frequency Response Hearing Aids
 
Author Affiliations & Notes
  • David A. Fabry
    Department of Communication Disorders, University of Minnesota
  • Dianne J. Van Tasell
    Department of Communication Disorders, University of Minnesota
  • Requests for reprints should be sent David A. Fabry, L-5 Audiology, Mayo Building, Mayo Clinic, Rochester, MN 56905.
  • Currently affiliated with Mayo Clinic, Rochester, MN.
    Currently affiliated with Mayo Clinic, Rochester, MN.×
Article Information
Research Articles
Research Article   |   December 01, 1990
Evaluation of an Articulation-Index Based Model for Predicting the Effects of Adaptive Frequency Response Hearing Aids
Journal of Speech, Language, and Hearing Research, December 1990, Vol. 33, 676-689. doi:10.1044/jshr.3304.676
History: Received January 29, 1990 , Accepted May 17, 1990
 
Journal of Speech, Language, and Hearing Research, December 1990, Vol. 33, 676-689. doi:10.1044/jshr.3304.676
History: Received January 29, 1990; Accepted May 17, 1990

The Articulation Index (AI) was used to evaluate an “adaptive frequency response” (AFR) hearing aid with amplification characteristics that automatically change to become more high-pass with increasing levels of background noise. Speech intelligibility ratings of connected discourse by normal-hearing subjects were predicted well by an empirically derived AI transfer function. That transfer function was used to predict aided speech intelligibility ratings by 12 hearing-impaired subjects wearing a master hearing aid with the Argosy Manhattan Circuit enabled (AFR-on) or disabled (AFR-off). For all subjects, the AI predicted no improvements in speech intelligibility for the AFR-on versus AFR-off condition, and no significant improvements in rated intelligibility were observed. The ability of the AI to predict aided speech intelligibility varied across subjects. However, ratings from every hearing-impaired subject were related monotonically to AI. Therefore, AI calculations may be used to predict relative—but not absolute—levels of speech intelligibility produced under different amplification conditions.

ACKNOWLEDGMENTS
This research was supported by NINCDS NS12125 and the Bryng Bryngelson Communication Disorders Fund at the University of Minnesota. We wish to acknowledge D. Preves and Argosy Electronics for providing the master hearing aid. C. Speaks, M. Ruggero, W. D. Ward, B. Waiden, and M. Leek provided valuable comments on this and earlier versions of the manuscript.
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