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Perceptual Acclimatization Post Nonlinear Frequency Compression Hearing Aid Fitting in Older Children

2012· article· en· W2138896885 on OpenAlexafffund
Danielle Glista, Susan Scollie, Jacob Sulkers

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsAudiologyPerceptionSpeech perceptionAcclimatizationPsychologyHearing aidConsonantCompression (physics)Speech recognitionMedicineVowelComputer science

Abstract

fetched live from OpenAlex

PURPOSE: In this study, the authors evaluated the effect of frequency compression hearing aids on speech perception ability and the time course and magnitude of acclimatization-related changes. METHOD: Participants included children ages 11-18 years. Speech perception ability was evaluated over well-controlled baseline, treatment, and withdrawal study phases. Study-worn hearing aids were individually fitted to all participants. The authors evaluated speech perception ability using outcomes of speech detection (/s/ and /[symbol in text]/ sounds), /s-[symbol in text]/ discrimination, and plural and consonant recognition. RESULTS: Indices of change were discussed on a case-by-case basis across all study phases. Significant treatment effects were measured for all cases, on at least one measure, with some listeners displaying significant acclimatization trends following a trial of frequency compression. CONCLUSION: Findings suggest that frequency compression provided varying outcomes, both in benefit and acclimatization, across listeners. For some, a period of acclimatization was necessary before change could be measured. For others, performance remained stable over the time course under evaluation, suggesting that some but not all children will experience improved speech recognition ability after a period of frequency compression hearing aid use.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.660
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.394
Teacher spread0.322 · 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 teacher head, 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

Citations53
Published2012
Admission routes2
Has abstractyes

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