Objectives: This study tested whether speech perception and spatial acuity improved in people with single-sided deafness and a cochlear implant (SSD+CI) when the frequency allocation table (FAT) of the CI was adjusted to optimize frequency-dependent sensitivity to binaural disparities. Design: Nine SSD+CI listeners with at least 6 months of CI listening experience participated. Individual experimental FATs were created to best match the frequency-to-place mapping across ears using either sensitivity to binaural temporal-envelope disparities or estimated insertion depth. Spatial localization ability was measured, along with speech perception in spatially collocated or separated noise, first with the clinical FATs and then with the experimental FATs acutely and at 2-month intervals for 6 months. Listeners then returned to the clinical FATs and were retested acutely and after 1 month to control for long-term learning effects. Results: The experimental FAT varied between listeners, differing by an average of 0.15 octaves from the clinical FAT. No significant differences in performance were observed in any of the measures between the experimental FAT after 6 months and the clinical FAT one month later, and no clear relationship was found between the size of the frequency-allocation shift and perceptual changes. Conclusion: Adjusting the FAT to optimize sensitivity to interaural temporal-envelope disparities did not improve localization or speech perception. The clinical frequency-to-place alignment may already be sufficient, given the inherently poor spectral resolution of CIs. Alternatively, other factors, such as temporal misalignment between the two ears, may need to be addressed before any benefits of spectral alignment can be observed.
Bibliographical notePublisher Copyright:
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- Binaural interactions
- Cochlear implant
- Frequency-to-place mapping
- Single-sided deafness
- Speech perception in noise
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't