Quantifying the benefits of sentence repetition on the intelligibility of speech in continuous and fluctuating noises
Bibliographic record
Abstract
Good verbal communication is essential to ensure safety in the workplace and social participation during daily activities. In many situations, speech comprehension is difficult due to hearing problems, the presence of noise, or other factors. As a result, listeners must often ask the speaker to repeat what was said in order to understand the complete message. However, there has been little research describing the exact benefits of this commonly used strategy. This study reports original data quantifying the effect of sentence repetition on speech intelligibility as a function of signal-to-noise ratio and noise type. Speech intelligibility data were collected using 18 normal-hearing individuals. The speech material consisted of the sentences from the Hearing In Noise Test (HINT) presented in modulated and unmodulated noises. Results show that repeating a sentence decreases the speech reception threshold (SRT), as expected, but also increases the slope of the intelligibility function. Repetition was also found to be more beneficial in modulated noises (decrease in SRT by 3.2 to 5.4 dB) than in the unmodulated noise (decrease in SRT by 2.0 dB). The findings of this study could be useful in a wider context to develop predictive tools to assess speech comprehension under various conditions.
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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 | 0.012 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".