Effects of cross-language voice training on speech perception: Whose familiar voices are more intelligible?
Bibliographic record
Abstract
Previous research has shown that familiarity with a talker's voice can improve linguistic processing (herein, "Familiar Talker Advantage"), but this benefit is constrained by the context in which the talker's voice is familiar. The current study examined how familiarity affects intelligibility by manipulating the type of talker information available to listeners. One group of listeners learned to identify bilingual talkers' voices from English words, where they learned language-specific talker information. A second group of listeners learned the same talkers from German words, and thus only learned language-independent talker information. After voice training, both groups of listeners completed a word recognition task with English words produced by both familiar and unfamiliar talkers. Results revealed that English-trained listeners perceived more phonemes correct for familiar than unfamiliar talkers, while German-trained listeners did not show improved intelligibility for familiar talkers. The absence of a processing advantage in speech intelligibility for the German-trained listeners demonstrates limitations on the Familiar Talker Advantage, which crucially depends on the language context in which the talkers' voices were learned; knowledge of how a talker produces linguistically relevant contrasts in a particular language is necessary to increase speech intelligibility for words produced by familiar talkers.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".