Exploring the role of singing, semantics, and amusia screening in speech-in-noise perception in musicians and non-musicians
Bibliographic record
Abstract
Sentence repetition has been the focus of extensive psycholinguistic research. The notion that music training can bolster speech perception in adverse auditory conditions has been met with mixed results. In this work, we sought to gauge the effect of babble noise on immediate repetition of spoken and sung phrases of varying semantic content (expository, narrative, and anomalous), initially in 100 English-speaking monolinguals with and without music training. The two cohorts also completed some non-musical cognitive tests and the Montreal Battery of Evaluation of Amusia (MBEA). When disregarding MBEA results, musicians were found to significantly outperform non-musicians in terms of overall repetition accuracy. Sung targets were recalled significantly better than spoken ones across groups in the presence of babble noise. Sung expository targets were recalled better than spoken expository ones, and semantically anomalous content was recalled more poorly in noise. Rerunning the analysis after eliminating thirteen participants who were diagnosed with amusia showed no significant group differences. This suggests that the notion of enhanced speech perception-in noise or otherwise-in musicians needs to be evaluated with caution. Musicianship aside, this study showed for the first time that sung targets presented in babble noise seem to be recalled better than spoken ones. We discuss the present design and the methodological approach of screening for amusia as factors which may partially account for some of the mixed results in the field.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".