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Record W2025025289 · doi:10.1121/1.1518983

Direct magnitude estimation and interval scaling of pleasantness and severity in dysphonic and normal speakers

2002· article· en· W2025025289 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsAudiologyPerceptionPsychologySpeech perceptionInterval (graph theory)Multidimensional scalingAuditory perceptionStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the validity of voice pleasantness and overall voice severity ratings of dysphonic and normal speakers using direct magnitude estimation (DME) and equal-appearing interval (EAI) auditory-perceptual scaling procedures. Twelve naive listeners perceptually evaluated voice pleasantness and severity from connected speech samples produced by 24 adult dysphonic speakers and 6 normal adult speakers. A statistical comparison of the two auditory-perceptual scales yielded a linear relationship representative of a metathetic continuum for voice pleasantness. A statistical relationship that is consistent with a prothetic continuum was revealed for ratings of voice severity. These data provide support for the use of either DME or EAI scales when making auditory-perceptual judgments of pleasantness, but only DME scales when judging overall voice severity for dysphonic speakers. These results suggest further psychophysical study of perceptual dimensions of voice and speech must be undertaken in order to avoid the inappropriate and invalid use of EAI scales used in the auditory-perceptual evaluation of the normal and dysphonic voice.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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