Leveraging the uniformity framework to examine crosslinguistic similarity for long-lag stops in spontaneous Cantonese-English bilingual speech
Bibliographic record
Abstract
While crosslinguistic influence is widespread in bilingual speech production, it is less clear which aspects of representation are shared across languages, if any. Most prior work examines phonetically distinct yet phonologically similar sounds, for which phonetic convergence suggests a cross-language link within individuals [1]. Convergence is harder to assess when sounds are already similar, as with English and Cantonese initial long-lag stops. Here, the articulatory uniformity framework [2, 3, 4] is leveraged to assess whether bilinguals share an underlying laryngeal feature across languages, and describe the nature of cross-language links. Using the SpiCE corpus of spontaneous Cantonese-English bilingual speech [5], this paper asks whether Cantonese-English bilinguals exhibit uniform voice-onset time for long-lag stops within and across languages. Results indicate moderate patterns of uniformity within-language—replicating prior work [2, 6]—and weaker patterns across languages. The analysis, however, raises many questions, as correlations were generally lower compared to prior work, and talkers did not adhere to expected ordinal VOT relationships by place of articulation. Talkers also retained clear differences for /t/ and /k/, despite expectations of similarity. Yet at the same time, more of the overall variation seems to derive from individual-specific differences. While many questions remain, the uniformity framework shows promise.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".