Modified Multiple Stimulus With Hidden Reference and Anchors–Gabrielsson Total Impression Sound Quality Rating Comparisons for Speech in Quiet, Noise, and Reverberation
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to obtain, analyze, and compare subjective sound quality data for the same test stimuli using modified multistimulus MUSHRA (Multiple Stimulus with Hidden Reference and Anchors) based procedures (viz., MUSHRA with custom anchors and MUSHRA without anchor) and the single-stimulus Gabrielsson's total impression rating procedure. METHOD: Twenty normally hearing young adults were recruited in this study. Participants completed sound quality ratings on two different hearing aid recording data sets-Data Set A contained speech recordings from four different hearing aids under a variety of noisy and processing conditions, and Data Set B contained speech recordings from a single hearing aid under a combination of different noisy, reverberant, and signal processing conditions. Recordings in both data sets were rated for their quality using the total impression rating procedure. In addition, quality ratings of Data Set A recordings were obtained using a MUSHRA with custom anchors, while the ratings of Data Set B recordings were collected using a MUSHRA without anchor. RESULTS: Statistical analyses revealed a high test-retest reliability of quality ratings for the same stimuli that were rated multiple times. In addition, high-interrater reliability was observed with all three rating procedures. Further analyses indicated (a) a high correlation between the total impression rating and the two modified MUSHRA ratings and (b) a similar relationship between the average and standard deviation of the subjective rating data obtained by the total impression rating and MUSHRA with custom anchors on Data Set A, and the total impression rating and the MUSHRA without anchor on Data Set B. CONCLUSION: Both sound quality procedures, namely, the MUSHRA-based procedures and the total impression rating scale, obtained similar quality ratings of varied hearing aid speech recordings with high reliability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".