title: Evaluation of an articulation-index based model for predicting the effects of adaptive frequency response hearing aids.
abstract: 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.
authors: Department of Communication Disorders, University of Minnesota.
authors: D A
authors: DA
authors: Fabry
authors: D J
authors: DJ
authors: Van Tasell
mesh_terms: D000328:Adult; D005069:Evaluation Studies as Topic; D006310:Hearing Aids / Q000592:standards*; D006319:Hearing Loss, Sensorineural / Q000503:physiopathology / Q000628:therapy*; D006801:Humans; D008954:Models, Biological; D009622:Noise; D011237:Predictive Value of Tests; D013063:Speech Discrimination Tests / Q000295:instrumentation* / Q000592:standards; D013065:Speech Intelligibility; D013067:Speech Perception