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Record W2346146769 · doi:10.1121/1.4950634

Preliminary acoustic descriptions of the pharyngeals and sosterior plosives of Northern Haida

2016· article· en· W2346146769 on OpenAlexaff
Corey Telfer, Jordan Lachler

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcousticsGeologySpeech recognitionComputer sciencePhysics

Abstract

fetched live from OpenAlex

Haida is a highly endangered language spoken on the archipelago of Haida Gwaii, off the coast of British Columbia, as well as in communities in Southeast Alaska. Like many languages of the Northwest Coast, Haida plosives include three manners of articulation: voiceless aspirated, voiceless unaspirated, and ejective. Analysis of recordings indicates that the Voice Onset Time of the posterior aspirated and ejective stops is nearly identical. It appears that these speech sounds differ primarily in their acoustic intensities, and a new measure called Burst Intensity Slope is proposed to quantify this difference. In addition, the Northern dialect has been described as including pharyngeal speech sounds (e.g., Krauss 1979, Enrico 1991); however, only one small acoustic study has been conducted to verify this (Bessell 1993). Using recordings of a small number of speakers, this paper aims to document the different types of pharyngeals using acoustic measurements. Of special interest is the pharyngeal plosive of Massett Haida, which often includes what appears to be concomitant aryepiglottal trilling (“growl voice”). This will be investigated by comparing the number of zero-count crossings with those of other types of plosives and vowels produced by the same 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 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)0.000
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.302
Teacher spread0.275 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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