Faculty Bibliography

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. $$:

Although cochlear implants improve the ability of profoundly deaf children to understand speech, critics claim that the published literature does not document even a single case of a child who has developed a linguistic system based on input from an implant. Thus, it is of clinical and scientific importance to determine whether cochlear implants facilitate the development of English language skills. The English language skills of prelingually deaf children with cochlear implants were measured before and after implantation. We found that the rate of language development after implantation exceeded that expected from unimplanted deaf children (p < .001) and was similar to that of children with normal hearing. Despite a large amount of individual variability, the best performers in the implanted group seem to be developing an oral linguistic system based largely on auditory input obtained from a cochlear implant

A 'multidimensional phoneme identification' (MPI) model is proposed to account for vowel perception by cochlear implant users. A multidimensional extension of the Durlach-Braida model of intensity perception, this model incorporates an internal noise model and a decision model to account separately for errors due to poor sensitivity and response bias. The MPI model provides a complete quantitative description of how listeners encode and combine acoustic cues, and how they use this information to determine which sound they heard. Thus, it allows for testing specific hypotheses about phoneme identification in a very stringent fashion. As an example of the model's application, vowel identification matrices obtained with synthetic speech stimuli (including 'conflicting cue' conditions [Dorman et al., J. Acoust. Soc. Am. 92, 3428-3432 (1992)] were examined. The listeners were users of the 'compressed-analog' stimulation strategy, which filters the speech spectrum into four partly overlapping frequency bands and delivers each signal to one of four electrodes in the cochlea. It was found that a simple model incorporating one temporal cue (i.e., an acoustic cue based only on the time waveforms delivered to the most basal channel) and spectral cues (based on the distribution of amplitudes among channels) can be quite successful in explaining listener responses. The new approach represented by the MPI model may be used to obtain useful insights about speech perception by cochlear implant users in particular, and by all kinds of listeners in general