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Speech sounds exist in a complex acoustic–phonetic space, and listeners vary in the extent to which they are sensitive to variability within the speech sound category (“gradience”) and the degree to which they show stable, consistent responses to phonetic stimuli. Here, we investigate the hypothesis that individual differences in the perception of the sound categories of one's language may aid speech-in-noise performance across the adult lifespan. Declines in speech-in-noise performance are well documented in healthy aging, and are, unsurprisingly, associated with differences in hearing ability. Nonetheless, hearing status and age are incomplete predictors of speech-in-noise performance, and long-standing research suggests that this ability draws on more complex cognitive and perceptual factors. In this study, a group of adults ranging in age from 18 to 67 years performed online assessments designed to measure phonetic category sensitivity, questionnaires querying recent noise exposure history and demographic factors, and crucially, a test of speech-in-noise perception. Results show that individual differences in the perception of two consonant contrasts significantly predict speech-in-noise performance, even after accounting for age and recent noise exposure history. This finding supports the hypothesis that individual differences in sensitivity to phonetic categories mediates speech perception in challenging listening situations.
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Noise schemas aid hearing in noise
Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise were better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts (adaptation increases over time and online noise estimation should benefit from acquiring more samples). However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest that noise robustness is supported by internal models—“noise schemas”—that are rapidly estimated, stored over time, and used to estimate other concurrent sounds.
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- Award ID(s):
- 2240406
- PAR ID:
- 10555303
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 121
- Issue:
- 47
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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