How does aging affect recognition of spectrally degraded speech?

Abstract

Objectives/Hypothesis: Cochlear implants (CIs) restore auditory sensation to patients with moderate-to-profound sensorineural hearing loss. However, the benefits to speech recognition vary considerably among patients. Advancing age contributes to this variability in postlingual adult CI users. Similarly, older individuals with normal hearing (NH) perform more poorly on tasks of recognition of spectrally degraded speech. The overarching hypothesis of this study was that the detrimental effects of advancing age on speech recognition can be attributed both to declines in auditory spectral resolution as well as declines in cognitive functions. Study Design: Case-control study. Methods: Speech recognition was assessed in CI users (in the clear) and NH controls (spectrally degraded using noise-vocoding), along with auditory spectral resolution using the Spectral–Temporally Modulated Ripple Test. Cognitive skills were assessed using nonauditory visual measures of working memory, inhibitory control, speed of lexical/phonological access, nonverbal reasoning, and perceptual closure. Linear regression models were tested for mediation to explain aging effects on speech recognition performance. Results: For both groups, older age predicted poorer sentence and word recognition. The detrimental effects of advancing age on speech recognition were partially mediated by declines in spectral resolution and in some measures of cognitive function. Conclusions: Advancing age contributes to poorer recognition of degraded speech for CI users and NH controls through declines in both auditory spectral resolution and cognitive functions. Findings suggest that improvements in spectral resolution as well as cognitive improvements may serve as therapeutic targets to optimize CI speech recognition outcomes.

High- and Low-Performing Adult Cochlear Implant Users on High-Variability Sentence Recognition: Differences in Auditory Spectral Resolution and Neurocognitive Functioning

“…Nonverbal reasoning has not consistently emerged as a significant predictor of speech recognition in CI users (e.g., Akeroyd; 1 but see Knutson et al; 24 Holden et al) 21 . Moberly et al (2018) 35 found that the strongest neurocognitive predictor of speech recognition among a similar group of CI users was nonverbal reasoning, using the same Raven's Progressive Matrices test as in the present study. In both studies, a timed version of the Raven's test was usedparticipants had to complete as many items as possible within ten minutes.…”

“…All of the CI users had participated in a larger study on speech, language, and neurocognitive skills, which, at the time of the present study, included 44 adult postlingually deafened CI users (Moberly et al; 33 Kramer et al; 25 Moberly et al). 35 Of the 21 CI users in the present study, 11 scored within the upper quartile of the distribution (HiPRESTO) and 10 scored within the lower quartile (LoPRESTO) of a group of 44 CI users (overlapping with participant groups reported in earlier studies) on PRESTO, after adjusting for those who did not complete or were unable to complete the SMRT (n ¼ 4). The 11 HiPRESTO CI users included 5 female and 6 male participants, aged 45-70 years (M ¼ 60; standard deviation [SD] ¼ 8), and the 10 LoPRESTO CI users included 4 female and 6 male participants, aged 52-81 years (M ¼ 65; SD ¼ 10).…”

“…Studies using acoustic vocoder simulations of CI hearing have demonstrated that speech perception and recognition declines with decreasing spectral resolution (i.e., with decreasing number of spectral channels) (e.g., Fu et al; 16 Friesen et al) 14 . In addition, for individual CI users, differences in spectral resolution have been found to be related to individual differences in vowel and consonant perception (Henry et al) 19 , word recognition (Won et al; 58 Drennen et al) 12 , and sentence recognition (Moberly et al) 35 , especially in noise (Won et al) 59 . The role of reduced spectral resolution may be further strengthened in conditions with high talker variability because listeners are forced to rely primarily on bottom-up cues when perceptual normalization for talker and accent variability is difficult (Clopper) 7 .…”

“…For detailed information on the general approach, materials, and collection procedures, see Moberly et al (2017), 33 Kramer et al (2018), 25 and Moberly et al (2018). 35…”

“…Differences in spectral resolution and speech recognition have typically been associated with patient-related demographic characteristics, such as age, age at implantation, duration of deafness, and residual hearing, as well as device-or surgicalrelated factors, such as implant model, implant settings, and surgical technique. Regardless of the underlying factors, poorer spectral resolution for some CI users may contribute to increased difficulty in recognizing speech (Henry et al; 19 Won et al; 59 Moberly et al) 35 . CI users with good spectral resolution may be better able to use the increased acoustic-phonetic spectral details provided by modern CI devices (Croghan et al) 9 .…”