Effect of number of masking talkers on masking of Chinese speech
Bibliographic record
Abstract
In this study, speech targets were nonsense sentences spoken by a Chinese female, and speech maskers were nonsense sentences spoken by other one, two, three, or four Chinese females. All stimuli were presented by two spatially separated loudspeakers. Using the precedence effect, manipulation of the delay between the two loudspeakers for the masker determined whether the target and masker were perceived as coming from the same or different locations. The results show that the one-talker masker produced the lowest masking effect on Chinese speech. When the number of masking talkers increased progressively to 2, 3, and 4, even though informational masking progressively decreased, energetic masking progressively increased, leading to an increased total masking effect on targets. A new form of calculation of the speech intelligibility index confirmed an increase of energetic masking as the masking-talker number increased, even when the long-term average signal-to-noise ratio was unchanged. Some differences between Chinese speech masking and English speech masking were revealed by this study. [Work supported by China NSF and Canadian IHR.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".