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Record W2546642275 · doi:10.3765/amp.v3i0.3684

Predicting Vowel Systems: An Acoustic Analysis of the Vowels of Mebêngôkre and Panará

2016· article· en· W2546642275 on OpenAlexafffund
Myriam Lapierre

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsVowelNasal vowelDimension (graph theory)Space (punctuation)Mid vowelMathematicsAcoustic spaceLinguisticsSpeech recognitionAcousticsAudiologyComputer sciencePhysicsAcoustic wavePure mathematicsFormantMedicinePhilosophy

Abstract

fetched live from OpenAlex

This paper has both a descriptive and a theoretical goal. The first is to provide novel typological data on two severely understudied languages of the Jê family, Mebêngôkre and Panará, through an acoustic analysis of the vowels of the two languages. The second is to determine whether the predictions made by the Dispersion-Focalization Theory of vowel systems (DFT, Schwartz et al. 1997a) can account for the organization of the vowel systems of these two languages. Acoustic results show that neither Mebêngôkre nor Panará has a true low nasal vowel, and that the acoustic space of the nasal vowel systems of both languages is reduced in the F1 dimension. The DFT fails to predict this typological observation, which is commonly observed in nasal vowel inventories of the world’s languages (Beddor 1982, Kingston 2007). The author proposes that the acoustic space of phonologically nasal vowels is constrained in the F1 dimension. This small modification to the DFT allows the principles governing the organization of oral vowel systems to apply normally in nasal vowels, albeit in a reduced space. This prediction is consistent with the data observed from natural languages, in which we observe a larger number of contrasts among the F2 dimension than the F1 dimension for nasal vowels.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.293
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 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

Citations2
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Annual Meetings on PhonologySame topicPhonetics and Phonology ResearchFrench-language works237,207