Objective: Quantify the relationship between average hours of processor use per day and measures of speech recognition in postlingually deafened adults with cochlear implants. Setting: Cochlear implant (CI) program at a tertiary medical centerPatients: Three hundred adult (mean age = 64, 130 females) CI users were included.Main Outcome Measures: Correlation analyses were completed for CI-aided speech recognition [Consonant-Nucleus-Consonant (CNC) monosyllables and AzBio sentences] at approximately 12 months post-implantation and average hours of processor use per day, which was extracted from the CI programming software.Results: Average processor use was 10.2 hours per day (range: 0.1 to 22.7), and average speech recognition scores were 49.9% and 61.7% for CNC and AzBio sentence recognition, respectively. We found a strong, significant correlation between hours of processor use per day and CNC word recognition (r s = 0.61, p<0.0001) and AzBio sentence recognition (r s = 0.56, p<0.0001). Conclusions:Results suggest that highest speech recognition outcomes are correlated with greater than 10 hours of CI use per day. Further research is needed to assess the causal link between daily CI use and speech recognition abilities.

Purpose-To compare speech intelligibility in the presence of a 10-Hz square-wave noise masker in younger and older listeners and to relate performance to recovery from forward masking.Method-The signal-to-noise ratio required to achieve 50% sentence identification in the presence of a 10-Hz square-wave noise masker was obtained for each of the 8 younger/older listener pairs. Listeners were matched according to their quiet thresholds for frequencies from 600 to 4800 Hz in octave steps. Forward masking was also measured in 2 younger/older threshold-matched groups for signal delays of 2-40 ms.Results-Older listeners typically required a significantly higher signal-to-noise ratio than younger listeners to achieve 50% correct sentence recognition. This effect may be understood in terms of increased forward-masked thresholds throughout the range of signal delays corresponding to the silent intervals in the modulated noise (e.g., <50 ms).Conclusions-Significant differences were observed between older and younger listeners on measures of both speech intelligibility in a modulated background and forward masking over a range of signal delays (0-40 ms). Age-related susceptibility to forward masking at relatively short delays may reflect a deficit in processing at a fairly central level (e.g., broader temporal windows or less efficient processing).Keywords forward masking; effects of age; modulated noise; speech recognition The National Institute on Deafness and Other Communication Disorders (NIDCD) reported that approximately one third of individuals over 60 years of age and over half of all individuals over 75 years live with a clinically significant hearing loss (NIDCD, 2001). Given those statistics, it is not surprising that a common complaint expressed by older individuals is difficulty understanding speech, particularly in a noisy background. However, recent research has shown that even those older listeners who have normal hearing sensitivity exhibit significant performance deficits on measures of speech recognition (Dubno, Horwitz, & Ahlstrom, 2002, 2003. Beyond the effects of reduced audibility, speech understanding is likely affected by an age-related impairment in temporal resolution (e.g

Cochlear implant (CI) surgery is considered standard of care treatment for severe hearing loss. CIs are currently programmed using a one-size-fits-all type approach. Individualized, position-based CI programming schemes have the potential to significantly improve hearing outcomes. This has not been possible because the position of stimulation targets is unknown due to their small size and lack of contrast in CT. In this work, we present a segmentation approach that relies on a weighted active shape model created using microCT scans of the cochlea acquired ex-vivo in which stimulation targets are visible. The model is fitted to the partial information available in the conventional CTs and used to estimate the position of structures not visible in these images. Quantitative evaluation of our method results in Dice scores averaging 0.77 and average surface errors of 0.15 mm. These results suggest that our approach can be used for position-dependent image-guided CI programming methods.