Voice quality as a marker of ethnicity in New Zealand: From acoustics to perception<sup>1</sup>
Bibliographic record
Abstract
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]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".