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Record W2134031337 · doi:10.1080/14015430050175860

Formal perceptual evaluation of voice quality in the United Kingdom

2000· article· en· W2134031337 on OpenAlexaff
Paul Carding, Eva Carlson, Ruth Epstein, Lesley Mathieson, Christina Shewell

Bibliographic record

VenueLogopedics Phoniatrics Vocology · 2000
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsTerminologyPerceptionQuality (philosophy)Reliability (semiconductor)Computer scienceVariety (cybernetics)Position statementScheme (mathematics)Statement (logic)Speech recognitionSound qualityProblem statementVoice analysisPsychologyArtificial intelligenceMedicineLinguisticsManagement scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

This document is a position statement on the formal perceptual evaluation of voice quality in the United Kingdom (UK). It addresses a number of clinical issues pertaining to the complexity of voice quality analysis. There is also a brief description of the three formal perceptual protocols most commonly used in the UK: The Vocal Profile Analysis (VPA), GRBAS and The Buffalo III Voice Profile. Potential clinical problems with perceptual voice quality evaluation are highlighted. Problems associated with the lack of defined terminology, limitless variety of voice quality, general lack of reliability data and difficulties in determining specificity and sensitivity are discussed. A practical guide for selecting an evaluation scheme is described. The conclusion is that the GRBAS scheme should be recommended as the absolute minimum standard for practising UK voice clinicians. However, there is a clear need to develop a more satisfactory perceptual rating scheme that is clinically realistic, theoretically sound, internationally acceptable and has proven reliability.

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)0.008
metaresearch head score (Gemma)0.027
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.109
GPT teacher head0.396
Teacher spread0.287 · 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

Citations92
Published2000
Admission routes1
Has abstractyes

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