Liu vs. Liu vs. Luke? Name influence on voice recall
Bibliographic record
Abstract
ABSTRACT Listeners are better at remembering voices speaking in familiar languages and accents, and this finding is often dubbed the language-familiarity effect (LFE). A potential mechanism behind the LFE relates to a combination of listeners’ implicit knowledge about lower level phonetic cues and higher level linguistic processes. While previous work has established that listeners’ social expectations influence various aspects of linguistic processing and speech perception, it remains unknown how such expectations might affect talker recognition. To this end, Mandarin-accented English voices and locally accented English voices were used in a talker recognition paradigm in conditions which paired voices with stereotypically congruent names (Mandarin-accented English voice as Chen and locally accented English voice as Connor) and stereotypically incongruent names (vice versa). Across two experiments, listeners showed greater recall for the familiar, local voices than the Mandarin-accented ones, confirming the basic premise of the LFE. Further, incongruent accent/name pairings negatively affected listeners’ performance, although listeners with experience speaking Mandarin were less influenced by the incongruent accent/name pairings. These results indicate that the LFE, while relying largely on listeners’ ability to parse linguistic information, is also affected by nonlinguistic information about a talker’s social identity.
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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 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".