Faculty Bibliography

Cochlear implant (CI) users differ in their ability to perceive and recognize speech sounds. Two possible reasons for such individual differences may lie in their ability to discriminate formant frequencies or to adapt to the spectrally shifted information presented by cochlear implants, a basalward shift related to the implant's depth of insertion in the cochlea. In the present study, we examined these two alternatives using a method-of-adjustment (MOA) procedure with 330 synthetic vowel stimuli varying in F1 and F2 that were arranged in a two-dimensional grid. Subjects were asked to label the synthetic stimuli that matched ten monophthongal vowels in visually presented words. Subjects then provided goodness ratings for the stimuli they had chosen. The subjects' responses to all ten vowels were used to construct individual perceptual 'vowel spaces.' If CI users fail to adapt completely to the basalward spectral shift, then the formant frequencies of their vowel categories should be shifted lower in both F1 and F2. However, with one exception, no systematic shifts were observed in the vowel spaces of CI users. Instead, the vowel spaces differed from one another in the relative size of their vowel categories. The results suggest that differences in formant frequency discrimination may account for the individual differences in vowel perception observed in cochlear implant users

This study examined two possible reasons underlying longitudinal increases in vowel identification by cochlear implant users: improved labeling of vowel sounds and improved electrode discrimination. The Multidimensional Phoneme Identification (MPI) model was used to obtain ceiling estimates of vowel identification for each subject, given his/her electrode discrimination skills. Vowel identification scores were initially lower than the ceiling estimates, but they gradually approached them over the first few months post-implant. Taken together, the present results suggest that improved labeling is the main mechanism explaining post-implant increases in vowel identification

On the basis of the good predictions for phonemes correct, we conclude that closed-set feature identification may successfully predict phoneme identification in an open-set word recognition task. For word recognition, however, the PCM model underpredicted observed performance, and the addition of a mental lexicon (ie, the SPAMR model) was needed for a good match to data averaged across 7 adults with CIs. The predictions for words correct improved with the addition of a lexicon, providing support for the hypothesis that lexical information is used in open-set spoken word recognition by CI users. The perception of words more complex than CNCs is also likely to require lexical knowledge (Frisch et al, this supplement, pp 60-62) In the future, we will use the performance off individual CI users on psychophysical tasks to generate predicted vowel and consonant confusion matrices to be used to predict open-set spoken word recognition. $$: