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Record W2050266243 · doi:10.3109/14992027.2013.791031

Development of the speech test signal in Brazilian Portuguese for real-ear measurement

2013· article· en· W2050266243 on OpenAlexafffund
Luciana Paula Garolla, Susan Scollie, Maria Cecília Martinelli Iório

Bibliographic record

VenueInternational Journal of Audiology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorOntario Innovation Trust
KeywordsAudiogramBrazilian PortuguesePortugueseAudiologySIGNAL (programming language)Test (biology)Speech recognitionSample (material)Computer scienceAcousticsHearing lossMedicineLinguisticsPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: Recommended practice is to verify the gain and/or output of hearing aids with speech or speech-shaped signals. This study has the purpose of developing a speech test signal in Brazilian Portuguese that is electroacoustically similar to the international long-term average speech spectrum (ILTASS) for use in real ear verification systems. DESIGN: A Brazilian Portuguese speech passage was recorded using standardized equipment and procedures for one female talker and compared to ISTS. The passage consisted of simple, declarative sentences making a total of 148 words. STUDY SAMPLE: The recordings of a Brazilian Portuguese passage were filtered to the ILTASS and compared to the International Speech Test Signal (ISTS). Aided recordings were made at three test levels, for three audiograms for the Brazilian Portuguese passage and the ISTS. RESULTS: The unaided test signals were spectrally matched to within 0.5 dB. Aided evaluation revealed that the Brazilian Portuguese passage produced aided spectra that were within 1 dB on average, within about 2 dB per audiogram, and within about 3 dB per frequency for 95% of fittings. CONCLUSION: Results indicate that the Brazilian Portuguese passage developed in this study provides similar electroacoustic hearing-aid evaluations to those expected from the standard ISTS passage.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.182

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 studies0.0000.000
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 head0.051
GPT teacher head0.309
Teacher spread0.258 · 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 designBench or experimental
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
Published2013
Admission routes2
Has abstractyes

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