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Record W3179062825 · doi:10.1097/aud.0000000000001078

The Influence of Sensation Level on Speech-Evoked Envelope Following Responses

2021· article· en· W3179062825 on OpenAlexafffund
Vijayalakshmi Easwar, Jen Birstler, Adrienne L. Harrison, Susan Scollie, David W. Purcell

Bibliographic record

VenueEar and Hearing · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesCanadian Institutes of Health Research
KeywordsAudiologySensationPsychologyEnvelope (radar)MedicineCognitive psychologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate sensation level (SL)-dependent characteristics of envelope following responses (EFRs) elicited by band-limited speech dominant in low, mid, and high frequencies. DESIGN: In 21 young normal hearing adults, EFRs were elicited by 8 male-spoken speech stimuli-the first formant, and second and higher formants of /u/, /a/ and /i/, and modulated fricatives, /∫/ and /s/. Stimulus SL was computed from behaviorally measured thresholds. RESULTS: At 30 dB SL, the amplitude and phase coherence of fricative-elicited EFRs were ~1.5 to 2 times higher than all vowel-elicited EFRs, whereas fewer and smaller differences were found among vowel-elicited EFRs. For all stimuli, EFR amplitude and phase coherence increased by roughly 50% for every 10 dB increase in SL between ~0 and 50 dB. CONCLUSIONS: Stimulus and frequency dependency in EFRs exist despite accounting for differences in audibility of speech sounds. The growth rate of EFR characteristics with SL is independent of stimulus and its frequency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.077
GPT teacher head0.317
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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