Singing in Noise: Can Music-Based Acoustic Features Aid Speech-in-Noise Comprehension?
Bibliographic record
Abstract
The ability to understand speech-in-noise (SPiN) can be improved with musical training, and music perception is resistant to age-related decline compared to other aspects of auditory cognition. This leads to the possibility that music-based forms of rehabilitation could improve SPiN comprehension. One possible approach to this putative rehabilitation program is to use a scaffolding technique, where a cognitive strength is used to scaffold a cognitive weakness. The first step in this line of research is to determine a source of auditory cognitive strength in SPiN comprehension. Accordingly, the goal of the current study was to determine if adding musical features to target speech could improve SPiN comprehension. Participants were presented with a series of sentences that were either spoken, sung, rapped, or sung with speech-like rhythm. Sentences were presented in noise, and the signal-to-noise ratio [SNR] was adapted based on accuracy. A 50% SNR threshold was determined for each condition. Overall, performance was best when target sentences were sung with speech-like rhythm, and worst when they were sung with musical rhythm. This pattern of results suggests that musical pitch contours can aid in understanding SPiN, and could potentially be used as a cognitive scaffold (i.e. a cognitive strength compared to understanding naturally spoken SPiN) to improve the ability to understand SPiN.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".