Articulation index (AI) theory was used to evaluate stop‐consonant recognition of normal‐hearing listeners and listeners with high‐frequency hearing loss. From results reported in a companion article [Dubno etal., J. Acoust. Soc. Am. 85, 347–354 (1989)], a transfer function relating the AI to stop‐consonant recognition was established, and a frequency importance function was determined for the nine stop‐consonant–vowel syllables used as test stimuli. The calculations included the rms and peak levels of the speech that had been measured in 1/3 octave bands; the internal noise was estimated from the thresholds for each subject. The AI model was then used to predict performance for the hearing‐impaired listeners. A majority of the AI predictions for the hearing‐impaired subjects fell within ±2 standard deviations of the normal‐hearing listeners’ results. However, as observed in previous data, the AI tended to overestimate performance of the hearing‐impaired listeners. The accuracy of the predictions decreased with the magnitude of high‐frequency hearing loss. Thus, with the exception of performance for listeners with severe high‐frequency hearing loss, the results suggest that poorer speech recognition among hearing‐impaired listeners results from reduced audibility within critical spectral regions of the speech stimuli.

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