Accent, intelligibility, and comprehensibility in the perception of foreign-accented Lombard speech
Bibliographic record
Abstract
Speech produced in noise (Lombard speech) has been reported to be more intelligible than speech produced in quiet (normal speech). This study examined the perception of non-native Lombard speech in terms of intelligibility, comprehensibility, and degree of foreign accent. Twelve Cantonese speakers and a comparison group of English speakers read simple true and false English statements in quiet and in 70 dB of masking noise. Lombard and normal utterances were mixed with noise at a constant signal-to-noise ratio, and presented along with noise-free stimuli to eight new English listeners who provided transcription scores, comprehensibility ratings, and accent ratings. Analyses showed that, as expected, utterances presented in noise were less well perceived than were noise-free sentences, and that the Cantonese speakers’ productions were more accented, but less intelligible and less comprehensible than those of the English speakers. For both groups of speakers, the Lombard sentences were correctly transcribed more often than their normal utterances in noisy conditions. However, the Cantonese-accented Lombard sentences were not rated as easier to understand than was the normal speech in all conditions. The assigned accent ratings were similar throughout all listening conditions. Implications of these findings will be discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".