Envelope-following responses elicited by modified speech sounds for estimating temporal processing dysfunction
Bibliographic record
Abstract
Speech is an ideal stimulus for eliciting envelope-following responses (EFR) given its inherent periodicities and biological importance. However, the speech EFR may reflect multiple aspects of temporal coding in the auditory nerve and brainstem driven by phase-locking to temporal fine-structure (TFS; carried predominantly by low-frequency speech harmonics) and the periodicity envelope (carried predominantly by unresolved high-frequency harmonics). This limits its utility as a measure of specific encoding deficits as a function of frequency. Multiple-fundamental frequency (multi-f0) speech sounds give rise to EFR related to narrow ranges of speech harmonics and thus may allow for assessment more specific with respect to frequency and type of temporal coding. This study investigated the relationship between the EFR obtained from multi-f0 speech, and individual differences in release from masking thought to reflect poor TFS coding. Multi-f0 EFR was measured in adults across a wide age-range with normal and near-normal hearing. Speech-in-noise thresholds were measured adaptively while manipulating talker f0 and spatial location using a virtual sound field. Results indicate that release from masking based on talker f0 is associated with the EFR elicited from only low-frequency speech harmonics, suggesting that speech-in-noise difficulties reflect distinct deficits in the encoding of TFS in the auditory periphery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".