The Differential Impact of Sleep-Dependent Consolidation in Speech Perceptual Learning: A Systematic Review.

Authors

Xinming Zhou, F Sayako Earle

Published in

Journal of speech, language, and hearing research : JSLHR. Pages 1-23. Sep 02, 2026. Epub Sep 02, 2026.

Abstract

Sleep plays a critical role in speech-sound learning. However, owing to methodological heterogeneity and inconsistent findings, the mechanisms by which sleep consolidates speech sounds are poorly understood. This review synthesizes evidence on sleep-dependent speech-sound consolidation to identify consistent behavioral patterns.
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we searched five major databases supplemented by reference list screening and expert consultation. We selected and reviewed 30 empirical articles representing 1,646 participants, primarily healthy young adults, in the full text for qualitative analysis. No meta-analysis was performed because of considerable variability in study designs and outcome measures.
Sleep robustly supports stabilization of fragile speech-sound traces from wake-dependent degradation, while enhancement and generalization emerge only conditionally. We observed enhancement effects in approximately half of the included studies, which were predominantly modulated by learner aptitude. Generalization occurred most consistently when the training variability was sufficient to support feature abstraction. Notably, the null findings tended to be correlated with signal degradation or elevated computational demands at encoding or assessment, suggesting that consolidation mechanisms may be resource limited and sensitive to encoding fidelity.
Taken together, our findings suggest that sleep acts as a selective filter. The brain prioritizes the stabilization of weakly encoded speech-sound traces, while allowing active reorganization only for representations that were robustly encoded. We propose a hierarchical framework to account for the observed patterns, along with translational research directions that leverage distinct learning pathways.

PMID:
42686109
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

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