Language-general versus language-specific processes in bilingual voice learning
Bibliographic record
Abstract
Language experience confers a benefit to voice learning, a concept described in the literature as the language familiarity effect (LFE). What experiences are necessary for the LFE to be conferred is less clear. We contribute empirically and theoretically to this debate by examining within and across language voice learning with Cantonese-English bilingual voices in a talker-voice association paradigm. Listeners were trained in Cantonese or English and assessed on their abilities to generalize voice learning at test on Cantonese and English utterances. By testing listeners from four language backgrounds - English Monolingual, Cantonese-English Multilingual, Tone Multilingual, and Non-tone Multilingual groups - we assess whether the LFE and group-level differences in voice learning are due to varying abilities (1) in accessing the relative acoustic-phonetic features that distinguish a voice, (2) learning at a given rate, or (3) generalizing learning of talker-voice associations to novel same-language and different-language utterances. The specific four language background groups allow us to investigate the roles of language-specific familiarity, tone language experience, and generic multilingual experience in voice learning. Differences in performance across listener groups shows evidence in support of the LFE and the role of two mechanisms for voice learning: the extraction and association of talker-specific, language-general information that is more robustly generalized across languages, and talker-specific, language-specific information that may be more readily accessible and learnable, but due to its language-specific nature, is less able to be extended to another language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".