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Record W2102716442 · doi:10.3109/14992020903121159

Children's speech perception and loudness ratings when fitted with hearing aids using the DSL v.4.1 and the NAL-NL1 prescriptions

2010· article· en· W2102716442 on OpenAlexaffabout
Susan Scollie, Teresa Y. C. Ching, Richard C. Seewald, Harvey Dillon, Louise Britton, Jane Steinberg, Katrina Agung King

Bibliographic record

VenueInternational Journal of Audiology · 2010
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsLoudnessAudiologyDigital subscriber linePsychologyConsonantNoise (video)MedicineSpeech recognitionComputer scienceVowel

Abstract

fetched live from OpenAlex

This paper reports speech and loudness measures on a group of children in a double-blind cross-over trial comparing the NAL-NL1 and DSL[i/o] prescriptions. Twenty-four children with hearing impairment were fitted with digital WDRC hearing aids at each site (Australia, Canada). Speech recognition was measured for nonsense syllables and for the 50% correct threshold for sentence recognition in noise. Loudness ratings for sentences were made on a 7-point scale. Measures were made at fitting and repeated following 8-week trials. Fitting orders were randomized and counterbalanced. Significant differences in consonant recognition occurred for individual children. On average, scores at the 80 dB SPL presentation level were better with the NAL-NL1 fitting. Loudness ratings differed at baseline but did not differ following home trials. Speech recognition scores revealed a small but significant interaction of prescription with level in quiet but not in noise. Individual children had significant performance differences. Loudness ratings showed significant acclimatization effects for children at both sites.SumarioEste trabajo reporta las mediciones de lenguaje y de intensidad subjetiva en un grupo de niños sometidos a un estudio cruzado doble ciego, que comparó las prescripciones NAL-NL1 y DSL. A 24 niños con problemas auditivos se les adaptaron auxiliares auditivos digitales con WDRC en cada sede (Australia, Canadá). El reconocimiento del lenguaje se midió con sílabas sin sentido y con el umbral correcto del 50% de reconoci-miento de oraciones en ruido. Los índices de intensidad subjetiva con oraciones se obtuvieron con base en una escala de 7 grados. Las mediciones se tomaron al hacer la adaptación y de manera repetida con pruebas cada 8 semanas. Las instrucciones de adaptación se manejaron con bases aleatorias y de contrapeso. Se encontraron diferencias significativas en el reconocimiento de consonantes en algunos niños. En promedio, las puntuaciones en el nivel de presentación de 80 dB SPL fueron mejores con la adaptación NAL-NL1. Los índices de intensidad subjetiva difirieron en el basal, pero no en las siguientes pruebas en el hogar. Las puntuaciones de reconocimiento del lenguaje revelaron una pequeña pero significativa interacción de la prescripción con el nivel sin ruido pero no con ruido. Los niños en lo individual tuvieron diferencias de rendimiento significativas. Los índices de intensidad subjetiva mostraron efectos de aclimatación en los niños, en las dos sedes.

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)0.002
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.286
Teacher spread0.266 · 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

Citations35
Published2010
Admission routes2
Has abstractyes

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