Cross-Language Differences in Informational Masking of Speech by Speech: English Versus Mandarin Chinese
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to determine why perceived spatial separation provides a greater release from informational masking in Chinese than English when target sentences in each of the languages are masked by other talkers speaking the same language. METHOD: Monolingual speakers of English and Mandarin Chinese listened to semantically anomalous sentences in their own language when 1 of 3 maskers was present (speech-spectrum noise, a 2-talker speech masker in the same language, and a 2-talker speech masker in the other language). RESULTS: Both groups benefitted equally from spatial separation when the maskers were speech-spectrum noise or cross-language. Chinese listeners benefitted less from spatial separation than did English listeners when a same-language masker was used. Performance was scored in terms of the number of target words correctly identified; because Chinese target words were composed of 2 "stand-alone" morphemes, the authors also scored Chinese target words as correct when either of the morphemes was correctly identified. When this was done, Chinese and English listeners benefitted equally from spatial separation in all conditions. CONCLUSION: These results support a model in which release from informational masking in both monolingual English and Chinese listeners occurs because spatial separation facilitates morpheme access in both languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".