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Voice quality as a marker of ethnicity in New Zealand: From acoustics to perception<sup>1</sup>

2012· article· en· W1511992017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Sociolinguistics · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEthnic groupPhonationPsychologyVowelAotearoaPerceptionAudiologyLinguisticsSociologyMedicineAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

This study is the first acoustic analysis of voice quality in the two main ethnic dialects of New Zealand English. In a production experiment, narratives from 36 speakers were analyzed and H1‐H2 spectral tilt measures were calculated for each vowel. The results provide instrumental evidence for impressionistic claims about the differing voice quality features of the two main ethnic groups, showing that Maori English speakers are creakier than European New Zealanders. A perception experiment was also carried out to determine the perceptual salience of voice quality for the identification of speaker ethnicity. The results of regression analyses confirm that listeners are sensitive to the phonation differences, and are able to rely on phonation cues in an ethnic dialect identification task. The study demonstrates the role of voice quality as a critical sociolinguistic variable, and highlights the importance of listeners’ previous dialect exposure in terms of sensitivity to prosodic cues. Ko tēnei pūrongo te tirohanga tuatahi ki ētahi āhuatanga o te reo e puta ana i te korokoro o ngā kaikōrero o ngā reo ā‐iwi e rua o te Reo Pākehā i Aotearoa. I tētahi whakamātau i āta tirohia ngā oropuare o ētahi kōrero nō ngā kaikōrero 36, ā, kua tatauria ngā ine H1‐H2 e kīia ana ko te spectral tilt. Ko ngā putanga he taunakitanga mō ngā whakaaro o te tāngata e pā ana ki te rerekētanga o ngā reo o te hunga Pākehā me te hunga Māori, e whakaatu hoki ana he kekē atu te reo o ngā mea e kōrero ana i te ‘Māori English’ i te reo e kōrerohia ana e te iwi Pākehā. He whakamātau whakarongo i whakahaerehia kia kite mena he āwhina tēnei āhuatanga o te reo kia mōhio te kaiwhakarongo ko wai te iwi o te kaikōrero. Ko ngā whakaputanga o ngā tatauranga e kī ana kei te tino mārama tēnei āhuatanga o te reo e puta ana i te korokoro o te tāngata, ā, ka taea ēnei rerekētanga te whakamahi kia whakawehea te tangata Māori i te tangata Pākehā. Nā tēnei ka kitea he āhuatanga motuhake te reo o te korokoro, ā, he mea nui hoki mena kua tino wāia te kaiwhakarongo ki ngā reo ā‐iwi. [Māori]

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.

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)0.003
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.030
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 head0.056
GPT teacher head0.392
Teacher spread0.336 · 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