Generalization of Perceptual Learning of Degraded Speech Across Talkers
Bibliographic record
Abstract
Purpose: We investigated whether perceptual learning of noise-vocoded (NV) speech is specific to a particular talker or accent. Method: Four groups of listeners (n = 18 per group) were first trained by listening to 20 NV sentences that had been recorded by a talker with either the same native accent as the listeners or a different regional accent. They then heard 20 novel NV sentences from either the native- or nonnative-accented talker (test), in a 2 × 2 (Training Talker per Accent × Test Talker per Accent) design. Results: Word-report scores at test for participants trained and tested with the same (native- or nonnative-accented) talker did not differ from those for participants trained with 1 talker per accent and tested on another. Conclusions: Learning of NV speech generalized completely between talkers. Two additional experiments confirmed this result. Thus, when listeners are trained to understand NV speech, they are not learning talker- or accent-specific features but instead are learning how to use the information available in the degraded signal. The results suggest that people with cochlear implants, who experience spectrally degraded speech, may not be too disadvantaged if they learn to understand speech through their implant by listening primarily to just 1 other talker, such as a spouse.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".