The segregation of target speech from competing speech when listening in a second language
Bibliographic record
Abstract
Introducing a perceived spatial separation (via the precedence effect) between target speech and competing speech reduces the signal-to-noise ratio (SNR) required for the recognition of key target words by 3-7 dB in syntactically-correct but semantically-anomalous target sentences such a "A rose could paint a fish" (target words: rose, paint, fish). A recent study of monolingual Mandarin participants listening to anomalous sentences in Mandarin Chinese suggests that perceived spatial separation releases listeners from informational masking (IM) by facilitating access to the lexicon at the morphemic level, presumably by enhancing the segregation of the target speech from the competing speech background. This raises the interesting question as to whether perceived spatial separation facilitates lexical access in the same way for people listening in the second language (L2) as it does when listening in their first language (L1) since access to the L2 lexicon is likely to be less robust than access to the L1 lexicon. A second interesting question is the extent to which operating in a L2 environment affects release from IM when the competing speech is in L1. These issues will be addressed in L2 English listeners whose L1 is either Chinese or Korean.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".