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Record W4205463476 · doi:10.3765/ptu.v6i1.5061

Acoustic Properties for the Kazakh Velar and Uvular Distribution

2021· article· en· W4205463476 on OpenAlexaff
Heather Lynn Yawney

Bibliographic record

VenueProceedings of the Workshop on Turkic and Languages in Contact with Turkic · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVoiceVowelDuration (music)KazakhPlace of articulationVoice-onset timeFormantSpeech recognitionLinguisticsConsonantAcousticsMathematicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Kazakh has an asymmetrical dorsal consonant inventory. Velars and uvulars are involved in two restrictions. First, the dorsal consonants are restricted in their place of articulation depending on neighbouring vowels. Velars appear in front vowel environments and uvulars appear in back vowel environment. Second, the dorsal consonants are restricted in voicing in the stem-final position. Voiceless velars and uvulars appear word-finally, while voiced velars and uvulars appear intervocalically with a following vowel-initial suffix. The existing descriptions regarding Kazakh dorsals contain limited amounts of data, and so an elicitation-based production experiment using nonce words with a native Kazakh speaker was conducted. Different acoustic properties, including closure duration, voice onset time, frication duration, centre of gravity, vowel duration, and F2 values, were examined with the purpose of determining whether the participant varied in their production of target velars and uvulars that could distinguish between place of articulation and voicing. The acoustic properties reveal that the place of articulation restriction is not productive, while the voicing restriction is productive.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.294
Teacher spread0.273 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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