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Record W2752635557 · doi:10.1080/14992027.2017.1358467

Preliminary evaluation of a novel non-linear frequency compression scheme for use in children

2017· article· en· W2752635557 on OpenAlexaboutno aff
Jace Wolfe, Mila Duke, Erin C. Schafer, Julia Rehmann, Siddhartha Jha, Silvia Allegro Baumann, Andrew John, Christine E. Jones

Bibliographic record

VenueInternational Journal of Audiology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsConsonantAudiologySpeech perceptionSpeech recognitionPerceptionComputer scienceSpeech processingHearing lossStop consonantTest (biology)PsychologyMedicineVowel

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary goal of this study was to evaluate a new form of non-linear frequency compression (NLFC) in children. The new NLFC processing scheme is adaptive and potentially allows for a better preservation of the spectral characteristics of the input sounds when compared to conventional NLFC processing. DESIGN: A repeated-measures design was utilised to compare the speech perception of the participants with two configurations of the new adaptive NLFC processing to their performance with the existing NLFC. The outcome measures included the University of Western Ontario Plurals test, the Consonant-Nucleus-Consonant word recognition test, and the Phonak Phoneme Perception test. STUDY SAMPLE: Study participants included 14 children, aged 6-17 years, with mild-to-severe low-frequency hearing loss and severe-to-profound high-frequency hearing loss. RESULTS: The results indicated that the use of the new adaptive NLFC processing resulted in significantly better average word recognition and plural detection relative to the conventional NLFC processing. CONCLUSION: Overall, the adaptive NLFC processing evaluated in this study has the potential to significantly improve speech perception relative to conventional NLFC processing.

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)0.005
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 score0.686
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0000.000

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 head0.126
GPT teacher head0.410
Teacher spread0.284 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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