Phonetic perception and production of coronal stops by adults exposed to two languages from birth
Bibliographic record
Abstract
In this dissertation, the phonetic production and perception abilities of simultaneous bilinguals exposed to both Canadian English (CE) and French (CF) from birth were tested to determine whether their abilities in the two languages are best described as autonomous or interdependent. For this purpose, the similarities and differences in production and perception of coronal stops, /d/ and /t/, in CE and CF by monolinguals and simultaneous bilinguals were investigated in three studies. Coronal stops in CE and CF are thought to differ subtly in place of articulation; CE coronal stops are transcribed as having an alveolar place of articulation whereas CF coronal stops are transcribed as having a dental place of articulation. In Study 1, the acoustics of coronal stops produced in isolated words in CE and CF by monolingual CE and CF speakers were analysed. Because there were no previous reports of acoustic characteristics of coronal stops in CE and CF, data from this study provide the foundation for comparisons of monolinguals and bilinguals in Studies 2 and 3. Results from Study 1 established that bursts associated with CE and CF stops differ in relative burst intensity and in burst spectral measures. In Study 2, acoustics of coronal stops embedded in sentences in CE and CF produced by simultaneous bilinguals and monolinguals was compared. Results indicated that production of CE and CF coronal stops by simultaneous bilinguals and monolinguals differs in that simultaneous bilinguals only used a subset of cues observed in cross-language comparison of monolingual productions. Finally, in Study 3, the discrimination of /dV/ syllables in CE and CF was compared across simultaneous bilinguals, monolingual CE speakers, monolingual CF speakers and early L2 learners of French. The simultaneous bilinguals were significantly better than monolingual CF speakers and early L2 learners of French. They were also better than the monolingual CE speakers, but only on one out of the two vowel contexts on which they were tested. Although there are similarities in the production and perception abilities of simultaneous bilinguals and monolingual speakers, the differences between them support an interdependent relationship between the two languages of bilinguals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".