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Record W2509201952 · doi:10.1250/ast.37.191

The perception of breathiness: Acoustic correlates and the influence of methodological factors

2016· article· en· W2509201952 on OpenAlexaff
Ilse B. Labuschagne, Valter Ciocca

Bibliographic record

VenueNippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · 2016
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBreathy voicePerceptionAcousticsPsychologySpeech recognitionAudiologyComputer sciencePhonationMedicinePhysics

Abstract

fetched live from OpenAlex

Research on the acoustic correlates of breathiness has been plagued by a lack of consistent findings across studies and low intra- and inter-rater agreement. Sources of variability can arise from different sources including: differences in stimulus types (recorded or synthesized); differences in speaker groups (for recorded stimuli) or in synthesis parameters (for synthesized stimuli); differences in experimental methodologies (task type, number of repetitions, listener backgrounds and experience). This review discussed these sources of variability, and described solutions that have the potential to address the variability and the inconsistencies often reported in the literature. A critical appraisal of the evidence about the relative importance of various acoustic measures resulted in the identification of measures of periodicity, noise content, and high-to-low frequency energy as the most likely acoustic correlates of breathiness.

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)0.025
metaresearch head score (Gemma)0.064
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.301
Teacher spread0.280 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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Same venueNippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo GakkaishiSame topicVoice and Speech DisordersFrench-language works237,207