Bibliographic record
Abstract
A listeners' ability to comprehend one speaker against a background of other speech-a phenomenon dubbed the cocktail party problem-varies according to the properties of the speech streams and the listener. Although a number of factors that contribute to a listener's ability to successfully segregate two simultaneous speech signals have been identified, comparably little work has focused on the role accents may play in this process. To this end, familiar Canadian-accented voices and unfamiliar British-accented voices were used in a competing talker task. Native speakers of Canadian English heard two different talkers simultaneously read sentences in the form of "[command] [colour] [preposition] [letter] [number] [adverb]" (e.g., "Lay blue at C4 now") and reported the coordinate from a target talker. Results indicate that on all but the most challenging trials, listeners did best when attending to an unfamiliar-accented target against a familiarly-accented masker and performed worse when forced to ignore the unfamiliar accent. These results suggest listeners can easily tune out a familiar accent, but are unable to do the same with an unfamiliar accent, indicating that unfamiliar accents are more effective maskers.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".