A cross-language familiar talker advantage
Bibliographic record
Abstract
Previous research has shown that familiar talkers are more intelligible than unfamiliar talkers. In the current study, we tested the source of this familiar talker advantage by manipulating the type of talker information available to listeners. Two groups of native English listeners were familiarized with the voices of five German-English bilingual talkers; one group learned the voices from German stimuli and the other from English stimuli. Thus, English-trained listeners had access to both language-independent and English-specific talker information, while German-trained listeners had access to language-independent and German-specific talker information. After three days of voice learning, all listeners performed a word recognition task in English. Consistent with previous findings, English-trained listeners found the speech of familiar talkers to be more intelligible than unfamiliar talkers, as measured by whole words and phonemes correct. In contrast, German-trained listeners showed no familiar talker advantage, suggesting that listeners must have knowledge of talker-specific, linguistically relevant information to elicit the familiar talker advantage and that knowledge of language-independent talker information - such as size and shape of the vocal tract - does not facilitate speech perception.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.015 | 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".