Bibliographic record
Abstract
Today’s hearing aids are sophisticated devices that use complex signal processing to alter the acoustic signal. As digital circuit complexity and power efficiency evolve, even more advanced processing algorithms will be possible and will need to be evaluated. Most existing measures of hearing aid processing involve global acoustic (e.g., Articulation Index) or global behavioural (e.g., Hearing in Noise Test) analyses. Such measures have not been shown sensitive enough to detect local acoustic or behavioural changes to individual speech segments that result from complex processing algorithms. The purpose of this study was to provide information to help in the development of a standardized test that can be used for phoneme by phoneme acoustic analysis and speech recognition for the purpose of evaluating the effects of complex hearing aid processing. Such a test would require clear acoustic boundaries for the onset and offset of each phoneme, which to date, have not been determined for semivowel sounds. Using items from the University of Western Ontario Distinctive Features Differences (UWODFD) test, I evaluated the acoustic boundaries at which the English intervocalic semivowels were just perceived by Canadian English listeners. This study aimed to 1) establish the acoustic onset of semivowel identification within the UWODFD items, and 2) evaluate whether that point could be predicted by magnitude of spectral change, formant pattern, and/or formant transition duration. Eight listeners were presented time-sliced UWODFD test tokens and were asked to identify the sound out of a list of 21. A multivariate regression was performed to determine the amount of variance accounted for by each predictor variable. The acoustic boundary for phoneme recognition was determined for each semivowel, using an operational definition of 75% correct recognition. This study successfully established the acoustic boundary for each semivowel. Different combinations of acoustic variables were needed to predict the recognition of different semivowel sounds, however formant ratios and transition duration consistently stood out to be important. No absolute ratio values or transition durations were found to identify the acoustic onsets, although a reduced range of ratio values was observed to separate perception and non-perception.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".