The Lombard intelligibility benefit of native and non-native speech for native and non-native listeners
Bibliographic record
Abstract
Speech produced in noise (Lombard speech) is more intelligible than speech produced in quiet (plain speech). Previous research on the Lombard intelligibility benefit focused almost entirely on how native speakers produce and perceive Lombard speech. In this study, we investigate the size of the Lombard intelligibility benefit of both native (American-English) and non-native (native Dutch) English for native and non-native listeners (Dutch and Spanish). We used a glimpsing metric to measure the energetic masking potential of speech, which predicted that both native and non-native Lombard speech could withstand greater amounts of masking to a similar extent, compared to plain speech. In an intelligibility experiment, native English, Spanish, and Dutch listeners listened to the same words, mixed with noise. While the non-native listeners appeared to benefit more from Lombard speech than the native listeners did, each listener group experienced a similar benefit for native and non-native Lombard speech. Energetic masking, as captured by the glimpsing metric, only accounted for part of the Lombard benefit, indicating that the Lombard intelligibility benefit does not only result from a shift in spectral distribution. Despite subtle native language influences on non-native Lombard speech, both native and non-native speech provides a Lombard benefit.
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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) | 0.000 | 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) | 0.003 | 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".