Quantifying the Lombard effect in different background noises
Bibliographic record
Abstract
The Lombard effect of increasing one’s vocal effort in the presence of background noise has been quantified by Pearsons et al. (1977) as a 0.6-dB increase in speech levels for each dB increase in the background noise up to a ceiling level. Lombard speech has also been investigated in other studies with variable results. This study reports data on the effect of different noises on (1) the slope of the function relating speech levels and noise levels and (2) the spectral structure of speech. Twenty normal-hearing adults were asked to read aloud ten sentences from the hearing in noise test (HINT) to an experimenter seated 1 m away, in quiet and in various noises (white, speech spectrum, babble, and restaurant) presented in the sound field at 60 and 75 dBA. Preliminary findings show that increases in speech levels in natural environmental noises (restaurant and babble) most closely follow Pearsons’ data, with a slope of 0.6 dB. In contrast, artificial noises (speech spectrum and white) were associated with lower slopes (0.2 and 0.4 dB, respectively). Findings of this study could be useful in a wider context of modeling the complete speech communication process from talker to listener.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".