Articulation Index Predictions Of Contextually Dependent Words

Three investigations were conducted to determine the application of the articulation index (AI) to the prediction of speech performance of hearing-impaired subjects as well as of normal-hearing listeners. Speech performance was measured in quiet and in the presence of two interfering signals for items from the Speech Perception in Noise test in which target words are either highly predictable from contextual cues in the sentence or essentially contextually neutral. As expected, transfer functions relating the AI to speech performance were different depending on the type of contextual speech material. The AI transfer function for probability-high items rises steeply, much as for sentence materials, while the function for probability-low items rises more slowly, as for monosyllabic words. Different transfer functions were also found for tests conducted in quiet or white noise rather than in a babble background. A majority of the AI predictions for ten individuals with moderate sensorineural loss fell within +/- 2 standard deviations of normal listener performance for both quiet and babble conditions.