Bibliographic record
Abstract
Previous study [C-N. Li, J. Acoust. Soc. Am. 114, 2364 (2003)] has shown that foreign-accented Lombard speech is more intelligible than normal speech when presented in noise to native English listeners. This research extends that work and examines the intelligibility of non-native English speakers’ Lombard speech perceived by listeners from the same L1 background. Twelve Cantonese speakers and a comparison group of English speakers read 48 simple true and false English sentences in quiet and in 70 dB of cafeteria noise. Normal and Lombard sentences were masked with noise at a constant signal-to-noise ratio, and presented along with noise-free stimuli to eight native Cantonese speakers who assessed intelligibility by transcribing the sentences in standard English orthography. Analyses indicated that for both groups of speakers, sentences presented in noise were less well perceived than those presented without noise. The Cantonese speakers’ utterances were more intelligible than were the native English productions. However, in noisy conditions, the Lombard speech of the Cantonese speakers was correctly transcribed less often than their normal utterances, and the English speakers’ Lombard speech was not more intelligible than their normal speech.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".