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Record W3000515213 · doi:10.1515/jsall-2019-2013

Kalasha affricates: An acoustic analysis of place contrasts

2019· article· en· W3000515213 on OpenAlexaff
Alexei Kochetov, Paul Arsenault

Bibliographic record

VenueJournal of South Asian Languages and Linguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsTyndale UniversityUniversity of Toronto
Fundersnot available
KeywordsFormantBreathy voiceContext (archaeology)SyllableLinguisticsSouth asiaSpeech recognitionVariation (astronomy)Duration (music)Computer scienceAcousticsHistoryVowelPhonationAstrophysicsPhysics

Abstract

fetched live from OpenAlex

Abstract Kalasha (Northwestern Indo-Aryan, spoken in Pakistan) exhibits a complex set of ten affricate phonemes, which is exceedingly rare among the world’s languages and not representative of the broader South Asian context. This paper presents results of an acoustic analysis of place contrasts (dental, retroflex, and alveolopalatal) in affricates of four laryngeal specifications (voiceless unaspirated, voiceless aspirated, non-breathy voiced, and breathy voiced). These consonants were produced by four male speakers of Kalasha in a variety of phonetic contexts, resulting in a sample of close to 700 affricate tokens. A series of acoustic analyses of the data revealed that place contrasts in Kalasha affricates are distinguished robustly by both burst/frication spectra and formant transitions, but not by duration, which correlates more with laryngeal features. Place distinctions are somewhat diminished for voiced affricates but are largely unaffected by aspiration and syllable position. Most of these results are consistent with what is known about comparable (yet laryngeally simpler) place contrasts in other languages outside of South Asia. However, some of them are unique and may reflect the typological uniqueness and complexity of Kalasha’s affricate system.

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)0.000
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 score0.217
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.348
Teacher spread0.333 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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