The native language benefit for voice recognition is not contingent on lexical access
Bibliographic record
Abstract
Listeners show heightened talker recognition for native compared to nonnative speech, formalized as the language familiarity effect (LFE) for voice recognition. Some findings suggest that language comprehension is the locus of the LFE, while others implicate expertise with the linguistic sound structure. These hypotheses yield different predictions for the LFE with time-reversed speech, a manipulation that precludes lexical access but preserves some indexical and phonetic properties. Research to date shows discrepant results for the LFE with this impoverished signal. Here we reconcile this discrepancy by examining how the amount of exposure to talkers’ voices influences the LFE for time-reversed speech. Three experiments were conducted. In all, two groups of English monolinguals were trained and then tested on the identification of four English talkers and four French talkers; one group heard natural speech and the other group heard time-reversed speech. Across the experiments, we manipulated exposure to the voices in terms of number of training trials and duration of the talkers’ sentences. A robust LFE emerged in all cases, though the magnitude was attenuated as the amount of exposure decreased. These results are consistent with the account that the LFE for talker identification is linked to the sound structure of language.
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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 | 0.000 | 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) | 0.005 | 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".