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