The role of language proficiency on the perception of speech and song
Bibliographic record
Abstract
During everyday communication, background noise can affect our ability to comprehend speech accurately. Although many studies have investigated perception of speech in noisy environments, it is less clear how our perception in noisy environments could be enhanced by song, which could be beneficial due to melodic/rhythmic predictability. Language proficiency may also impact perception of song and speech. The current study aimed to clarify the unique struggles that people with low language proficiency face. We used the Test of Adolescent and Adult Language - Fourth Edition (TOAL-4) to divide 48 participants into low and high-English proficiency groups. Participants’ speech-in-noise performance was assessed across four blocks of sung and spoken sentences. As predicted, performance improved for both song and speech over the course of the experiment and was higher in high-proficiency participants than low-proficiency English speakers. However, contrary to our predictions, participants found it harder to comprehend song than speech. This did not interact with language proficiency (high versus low). Overall, our findings suggest that, regardless of language background, song does not benefit perception in noise, challenging previous research that highlight the benefit of melodies and rhythms in facilitating speech perception in noisy or difficult listening environments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".