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Record W4394972366 · doi:10.3390/app14083447

Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers

2024· article· en· W4394972366 on OpenAlexaff
Jenna L. Bucci, Nedeljko Jovanovic, Philip C. Doyle

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsRoyal Victoria Regional Health CentreWestern University
Fundersnot available
KeywordsAudiologyPsychologyCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

Objective: This study examined the relationship between gender and auditory-perceptual evaluation of tracheoesophageal (TE) speech. Method: We collected auditory-perceptual judgments of two features, speech acceptability and listener comfort, from normal-hearing young adult listeners (n = 16) who were naïve to TE speech. Auditory-perceptual judgments were made for 12 TE speakers (6 men and 6 women) on two occasions separated by between 7 and 14 days. During the first session, listeners were deceived about the gender of the voice samples presented, and in the second session, listeners were informed of the true gender of the voice samples. Results: The findings suggest that a gender bias exists in perceptions of TE speech, and that female TE speakers tend to be disproportionately penalized when compared to their male counterparts when gender is known. Conclusions: These data provide insights into the potential influence of speaker gender on listener judgments of TE speech and the impact that such factors may have on communication. Our data indicate that listeners rate female TE speaker samples as less acceptable and less comfortable to listen to when the samples are known to be female speakers.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.640
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.120
GPT teacher head0.371
Teacher spread0.251 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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