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Record W4406369179 · doi:10.1121/10.0035236

Oral and nasal airflow in nasalized laryngeal consonants

2024· article· en· W4406369179 on OpenAlexaff
Matthew Faytak, Jorge Emilio Rosés Labrada, Lev Michael, Myriam Lapierre, Tyler T. Schnoor, Tianle Yang, Mariana Quintana Godoy, Beatriz Apolinario

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAirflowNasalizationAudiologyNasal vowelMedicineAcousticsComputer scienceSpeech recognitionPhysics

Abstract

fetched live from OpenAlex

While it has long been known that laryngeal consonants do not block nasal harmony, it has been debated whether the velum actually remains lowered during the production of glottals in nasal harmony contexts. Using newly collected nasal and oral airflow data from two Amazonian languages, Maihki (Tukanoan, Peru) and Piaroa (Jodi¨-Sáliban, Venezuela and Colombia), we demonstrate that the laryngeals [h, ʔ] are, in fact, produced as nasalized [h̃ , ̃ ʔ] in these languages when they occur in nasal harmony spans. We observe that nasal flow is greater for both intervocalic [h] and [ʔ] in a nasal harmony context than in a non-harmony (oral) context, suggesting a lowered velum for both segments conditioned by the nasal harmony span. The glottal stops examined lack complete closure, as indicated by their non-zero oral flow; we consider the implications of this for the relationship between glottal stricture and measured nasality. [Work supported by U.S. National Science Foundation Award #1918064.]

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.027
GPT teacher head0.348
Teacher spread0.321 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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