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. 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.