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Record W2410561165 · doi:10.1097/aud.0000000000000224

The Norwegian Hearing in Noise Test for Children

2015· article· en· W2410561165 on OpenAlexaboutno aff
Marte Myhrum, Ole Tvete, Mariann Gjervik Heldahl, Inger Moen, Sigfrid D. Soli

Bibliographic record

VenueEar and Hearing · 2015
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyQUIETNoise (video)NorwegianSpeech perceptionSentenceActive listeningPsychologyTest (biology)Speech recognitionMedicinePerceptionCommunicationComputer scienceLinguisticsNatural language processing

Abstract

fetched live from OpenAlex

In Brief Objectives: The aims of this study were to create 12 ten-sentence lists for the Norwegian Hearing in Noise Test for children, and to use these lists to collect speech reception thresholds (SRTs) in quiet and in noise to assess speech perception in normal hearing children 5 to 13 years of age, to establish developmental trends, and to compare the results with those of adults. Data were collected in an anechoic chamber and in an audiometric test room, and the effect of slight room reverberation was estimated. Design: The Norwegian Hearing in Noise Test for children was formed from a subset of the adult sentences. Selected sentences were repeatable by 5- and 6-year-old children in quiet listening conditions. Twelve sentence lists were created based on the sentences’ phoneme distributions. Six-year-olds were tested with these lists to determine list equivalence. Slopes of performance intensity (PI) functions relating mean word scores and signal to noise ratios (SNRs) were estimated for a group of 7-year-olds and adults. HINT normative data were collected for 219 adults and children 5 to 13 years of age in anechoic and audiometric test rooms, using noise levels 55, 60, or 65 dBA. Target sentences always originated from the front; whereas, the noise was presented either from the front, noise front (NF), from the right, noise right (NR) or from the left, noise left (NL). The NR and NL scores were averaged to yield a noise side (NS) score. All 219 subjects were tested in the NF condition, and 95 in the NR and NL conditions. Retest of the NF at the end of the test session was done for 53 subjects. Longitudinal data were collected by testing 9 children as 6, 8, and 13 years old. Results: NF and NS group means for adults were −3.7 and −11.8 dB SNR, respectively. Group means for 13-year-olds were −3.3 and −9.7, and for the 6-year-olds group means were −0.3 and −5.7 dB SNR, as measured in an anechoic chamber. NF SRTs measured in an audiometric test room were 0.7 to 1.5 higher (poorer) than in the anechoic chamber. Developmental trends were comparable in both rooms. PI slopes were 8.0% dB SNR for the 7-year-olds and 10.1% for the adults. NF SRTs in the anechoic chamber improved by 0.7 dB per year over an age range of 5 to 10 years. Using a PI slope 8 to 10% per dB, the estimated increase in percent intelligibility was 4 to 7% per year. Adult SRTs were about 3 dB lower than those for 6-year-olds, corresponding to 25 to 30% better intelligibility for adults. Conclusions: Developmental trends in HINT performance for Norwegian children with normal hearing are similar to those seen in other languages, including American English and Canadian French. SRTs approach adult normative values by the age of 13; however, the benefits of spatial separation of the speech and noise sources are less than those seen for adults. The Norwegian Hearing in Noise Test (NHINT) for children was developed, and NHINT Speech Reception Thresholds (SRTs) were collected for 219 normal-hearing children and adults. The sentences were presented from the front, whereas the noise was presented from the front (NF) or the side (NS) of the listener. There was a developmental trend in NF versus age between ages 5.5 to 10 years and in NS between ages 5.5 to 13 years. The NF and NS group means for the 6-year-olds were -0.3 and -5.7 dB compared to -3.3 and -9.7 dB for 13-year-olds and -3.7 and -11.8 dB for adults.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.216
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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