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Record W4388673574 · doi:10.1121/10.0022387

Spectral analysis of strident fricatives in cisgender and transfeminine speakers

2023· article· en· W4388673574 on OpenAlexfundno aff
Nichole Houle, Mackenzie P. Lerario, Susannah V. Levi

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
FundersNational Institute on Deafness and Other Communication DisordersYork UniversityNational Institutes of HealthFordham UniversityNew York University
KeywordsPsychologyExpansiveAudiologyPhysicsMedicine

Abstract

fetched live from OpenAlex

The spectral features of /s/ and /ʃ/ carry important sociophonetic information regarding a speaker's gender. Often, gender is misclassified as a binary of male or female, but this excludes people who may identify as transgender or nonbinary. In this study, we use a more expansive definition of gender to investigate the acoustics (duration and spectral moments) of /s/ and /ʃ/ across cisgender men, cisgender women, and transfeminine speakers in voiced and whispered speech and the relationship between spectral measures and transfeminine gender expression. We examined /s/ and /ʃ/ productions in words from 35 speakers (11 cisgender men, 17 cisgender women, 7 transfeminine speakers) and 34 speakers (11 cisgender men, 15 cisgender women, 8 transfeminine speakers), respectively. In general, /s/ and /ʃ/ center of gravity was highest in productions by cisgender women, followed by transfeminine speakers, and then cisgender men speakers. There were no other gender-related differences. Within transfeminine speakers, /s/ and /ʃ/ center of gravity and skewness were not related to the time proportion expressing their feminine spectrum gender or their Trans Women Voice Questionnaire scores. Taken together, the acoustics of /s/ and /ʃ/ may signal gender group identification but may not account for within-gender variation in transfeminine gender expression.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.322
Teacher spread0.294 · 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 designQualitative
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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicLinguistic Variation and MorphologyFrench-language works237,207