Cochlear implants can provide partial restoration of hearing, even with limited spectral resolution and loss of fine temporal structure, to severely deafened individuals. Studies have indicated that background noise has significant deleterious effects on the speech recognition performance of cochlear implant patients. This study investigates the effects of noise on speech recognition using acoustic models of two cochlear implant speech processors and several predictive signal-processing-based analyses. The results of a listening test for vowel and consonant recognition in noise are presented and analyzed using the rate of phonemic feature transmission for each acoustic model. Three methods for predicting patterns of consonant and vowel confusion that are based on signal processing techniques calculating a quantitative difference between speech tokens are developed and tested using the listening test results. Results of the listening test and confusion predictions are discussed in terms of comparisons between acoustic models and confusion prediction performance. © 2005 Hindawi Publishing Corporation.