Stop-consonant recognition for normal-hearing listeners and listeners with high-frequency hearing loss. II: Articulation index predictions

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 et al., 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.