A Laboratory Evaluation of Contextual Factors Affecting Ratings of Speech in Noise: Implications for Ecological Momentary Assessment
Bibliographic record
Abstract
OBJECTIVES: As hearing aid outcome measures move from retrospective to momentary assessments, it is important to understand how contextual factors influence subjective ratings. Under laboratory-controlled conditions, we examined whether subjective ratings changed as a function of acoustics, response timing, and task variables. DESIGN: Eighteen adults (age 21 to 85 years; M = 51.4) with sensorineural hearing loss were fitted with hearing aids. Sentences in noise were presented at 3 overall levels (50, 65, and 80 dB SPL) and 3 signal to noise ratios (0, +5, and +10 dB signal to noise ratio [SNR]). Listeners rated three sound quality dimensions (intelligibility, noisiness, and loudness) under four experimental conditions that manipulated timing and task focus. RESULTS: The quality ratings changed as the acoustics changed: intelligibility ratings increased with input level (p < 0.05); noisiness ratings increased at poorer SNRs (p < 0.05); and loudness ratings increased as input level increased (p < 0.05). Timing of rating was significant at the highest presentation level (80 dB SPL): Participants gave higher noise ratings while listening to the signal than afterward (p < 0.05). Presence of a secondary task had no significant effect on ratings (p > 0.1). CONCLUSIONS: The findings of this laboratory study provide evidence to support the conclusion that group-mean listener ratings of loudness, noisiness, and intelligibility change in predictable ways as level and SNR of the speech in noise stimulus are altered. They also provide weak evidence to support the conclusion that timing of the ratings (during or immediately after sound exposure) can affect noisiness ratings under certain conditions, but no evidence to support the conclusion that timing affects other quality ratings. There is also no evidence to support the conclusion that quality ratings are influenced by the presence of, or focus on, a secondary nonauditory task of the type used here.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".