Modeling phoneme and open-set word recognition by cochlear implant users: a preliminary report.
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.
Meyer
T A
TA
Department of Otolaryngology, Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, USA.
Frisch
S
S
Svirsky
M A
MA
Pisoni
D B
DB
D000328:Adult; D003054:Cochlear Implants; D006801:Humans; D008962:Models, Theoretical; D013067:Speech Perception