Neural encoding of speech-in-noise in neonates: A frequency-following response study
Bibliographic record
Abstract
Background noise disrupts the neural encoding of speech, making it particularly challenging to extract a speaker's voice from competing voices-an ability crucial for successful speech processing and communication. This disruption occurs across all ages, with infants and older adults being particularly vulnerable. In infancy, when robust speech encoding is fundamental for native language acquisition, the presence of background noise could have significant consequences for the development of speech and language processing. This study investigates the impact of background noise on the neural encoding of speech sounds in neonates. We recorded the frequency-following response to a /da/ syllable in both quiet and noise conditions from 25 healthy-term neonates and 21 normal-hearing adults. Results revealed higher neural responses in the adult group compared to newborns. Both groups exhibited reduced spectral amplitudes in the noise condition, with adults showing a greater decrease in the fundamental frequency spectral amplitude during the consonant transition compared to the steady vowel section. In contrast, neonates displayed similar disruption across both sections, possibly reflecting their immature auditory systems and limited exposure to higher-frequency formants in utero. This study represents a first step toward understanding the development of speech-in-noise processing from birth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".