The phonetics of code-switched vowels
Bibliographic record
Abstract
Aims and Objectives: This study investigates the effects of code-switching on vowel quality, pitch and duration among English–French bilinguals. Code-switching has been claimed to influence the morphology, syntax and lexicon, but not the phonology of the switched language. However, studies on voice-onset time have found subtle phonetic effects of code-switching, even though there are no categorical phonological effects. We investigate this further through the following three questions: (1) Are F1 and F2 influenced in the process of code-switching? (2) Are code-switched words hyper-articulated? (3) Does code-switching have an effect on vowel duration before voiced and voiceless consonants? Methodology: To address our research questions we relied on an insertional switching method where words from one language were inserted into carrier phrases of the other to simulate English–French code-switching environments. Bilingual speakers were recorded while they read code-switched sentences as well as sentences that did not involve code-switching, that is, monolingual sentences. Data and Analysis: The vowels of target words in the recorded utterances were compared – code-switched contexts against monolingual contexts – for vocalic duration, F0, F1 and F2. Findings/Conclusions: Like previous voice-onset time studies, our results indicate that code-switching does not shift the phonology to that of the embedded language. We did, however, find subtle lower level phonetic effects, especially in the French target words; we also found evidence of hyper-articulation in code-switched words. At the prosodic level, target switch-words approached the prosodic contours of the carrier phrases they are embedded in. Originality: The approach taken in this study is novel for its investigation of vowel properties instead of voice-onset time. Significance: This new approach to investigating code-switching adds to our understanding of how code-switching affects pronunciation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".