Language familiarity mediates identification of bilingual talkers across languages
Bibliographic record
Abstract
Many studies show that listeners are more accurate at identifying talkers in their native language than in an unfamiliar language; yet, little is known about the nature of this language familiarity effect in bilingual speech. Here, we investigate the links between language and talker processing further by assessing listeners’ ability to identify bilingual talkers across languages. Two groups were recruited: English monolinguals and English-French bilinguals. Participants learned to identify bilinguals speaking in only one language (English); they were then tested on their ability to identify the same talkers speaking in the trained language (same language context: English) and in their other language (different language context: French). Both monolinguals and bilinguals showed above chance performance in identifying talkers in both language contexts at test, confirming that there is sufficient information in bilingual speech to generalize across languages. Moreover, the results showed a language context effect that was facilitated by language familiarity: monolinguals showed a substantial decrease in performance between the same and different language contexts, whereas the bilinguals—who understood both languages at test—showed only a slight decrease in performance between the two language contexts. These results indicate that language familiarity affects talker encoding and retrieval, even for bilingual speech.
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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 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".