| Summary: | Understanding speech in noisy environments is often taken for granted; however, this task is particularly challenging for people with cochlear hearing loss, even with hearing aids or cochlear implants. A significant limitation to improving auditory prostheses is our lack of understanding of the neural basis for robust speech perception in noise. Perceptual studies suggest the slowly varying component of the acoustic waveform (envelope, ENV) is sufficient for understanding speech in quiet, but the rapidly varying temporal fine structure (TFS) is important in noise. These perceptual findings have important implications for cochlear implants, which currently only provide ENV; however, neural correlates have been difficult to evaluate due to cochlear transformations between acoustic TFS and recovered neural ENV. Here, we demonstrate the relative contributions of neural ENV and TFS by quantitatively linking neural coding, predicted from a computational auditory nerve model, with perception of vocoded speech in noise measured from normal hearing human listeners. Regression models with ENV and TFS coding as independent variables predicted speech identification and phonetic feature reception at both positive and negative signal-to-noise ratios. We found that: (1) neural ENV coding was a primary contributor to speech perception, even in noise; and (2) neural TFS contributed in noise mainly in the presence of neural ENV, but rarely as the primary cue itself. These results suggest that neural TFS has less perceptual salience than previously thought due to cochlear signal processing transformations between TFS and ENV. Because these transformations differ between normal and impaired ears, these findings have important translational implications for auditory prostheses.
|
|---|
