When Jack isn’t Jacques: Simultaneous opposite language-specific speech perceptual learning in French–English bilinguals
Bibliographic record
Abstract
Humans are remarkably good at understanding spoken language, despite the huge variability of the signal as a function of the talker, the situation, and the environment. This success relies on having access to stable representations based on years of speech input, coupled with the ability to adapt to short-term deviations from these norms, e.g. accented speech or speech altered by ambient noise. In the last two decades, there has been a robust research effort focused on a possible mechanism for adjusting to accented speech. In these studies, listeners typically hear 15 - 20 words in which a speech sound has been altered, creating a short-term deviation from its longer-term representation. After exposure to these items, listeners demonstrate "lexically driven phonetic recalibration"-they alter their categorization of speech sounds, expanding a speech category to take into account the recently heard deviations from their long-term representations. In the current study, we investigate such adjustments by bilingual listeners. French-English bilinguals were first exposed to nonstandard pronunciations of a sound (/s/ or /f/) in one language and tested for recalibration in both languages. Then, the exposure continued with both the original type of mispronunciation in the same language, plus mispronunciations in the other language, in the opposite direction. In a final test, we found simultaneous recalibration in opposite directions for the two languages-listeners shifted their French perception in one direction and their English in the other: Bilinguals can maintain separate adjustments, for the same sounds, when a talker's speech differs across two languages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".