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Record W2068197167 · doi:10.1121/1.3588032

An acoustic description of Maku vowels.

2011· article· en· W2068197167 on OpenAlexaff
Rebekka Puderbaugh, Benjamin V. Tucker

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVowelFormantMid vowelContext (archaeology)Stress (linguistics)Focus (optics)Acoustic spaceNasal vowelDuration (music)AcousticsSpeech recognitionComputer scienceMathematicsLinguisticsGeologyAcoustic wavePhysics

Abstract

fetched live from OpenAlex

Maku is believed to have originated in Venezuela and migrated into Brazil in the 19th century though there are no longer believed to be any living speakers. Field recordings of a single adult male speaker made in 1953 and 1965 by Migliazza [1978] are used in the current project for an acoustic analysis of the phonemic vowel inventory of Maku. Early impressions of the vowel space indicate five to seven vowels: /i, ī, u, e, o, a/ as well as the areally unique /y/, with possible length and nasal/oral vowel distinctions. All vowels and their surrounding context in the original recordings were identified and coded. Vowel duration and values for the first three formants at 25%, 50%, and 75% through the vowel were extracted. Vowel categories were modeled from formant values using conditional inference trees [Hothorn et al. (2006)]. These models showed the likelihood of five vowel categories on the basis of the formant measures as well as the length of the vowel segment. Additional analyzes focus on consonantal context, word position, stress and pitch. The acoustic data are also used to reconstruct the vowel space from the bottom up without the influence of investigators’ perceptual judgment.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.059
GPT teacher head0.328
Teacher spread0.269 · 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
Published2011
Admission routes1
Has abstractyes

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