MétaCan
Menu
Back to cohort
Record W2768027822 · doi:10.1044/2017_aja-17-0023

The Effect of Adaptive Nonlinear Frequency Compression on Phoneme Perception

2017· article· en· W2768027822 on OpenAlexaff
Danielle Glista, Marianne Hawkins, Andrea Bohnert, Julia Rehmann, Jace Wolfe, Susan Scollie

Bibliographic record

VenueAmerican Journal of Audiology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsAudiologyHearing aidSpeech perceptionPerceptionCrossover studySpeech recognitionPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

PURPOSE: This study implemented a fitting method, developed for use with frequency lowering hearing aids, across multiple testing sites, participants, and hearing aid conditions to evaluate speech perception with a novel type of frequency lowering. METHOD: A total of 8 participants, including children and young adults, participated in real-world hearing aid trials. A blinded crossover design, including posttrial withdrawal testing, was used to assess aided phoneme perception. The hearing aid conditions included adaptive nonlinear frequency compression (NFC), static NFC, and conventional processing. RESULTS: Enabling either adaptive NFC or static NFC improved group-level detection and recognition results for some high-frequency phonemes, when compared with conventional processing. Mean results for the distinction component of the Phoneme Perception Test (Schmitt, Winkler, Boretzki, & Holube, 2016) were similar to those obtained with conventional processing. CONCLUSIONS: Findings suggest that both types of NFC tested in this study provided a similar amount of speech perception benefit, when compared with group-level performance with conventional hearing aid technology. Individual-level results are presented with discussion around patterns of results that differ from the group average.

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)0.001
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 score0.912
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.318
Teacher spread0.299 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of AudiologySame topicHearing Loss and RehabilitationFrench-language works237,207