The role of visual speech information in supporting perceptual learning of degraded speech.
Bibliographic record
Abstract
Following cochlear implantation, hearing-impaired listeners must adapt to speech as heard through their prosthesis. Visual speech information (VSI; the lip and facial movements of speech) is typically available in everyday conversation. Here, we investigate whether learning to understand a popular auditory simulation of speech as transduced by a cochlear implant (noise-vocoded [NV] speech) is enhanced by the provision of VSI. Experiment 1 demonstrates that provision of VSI concurrently with a clear auditory form of an utterance as feedback after each NV utterance during training does not enhance learning over clear auditory feedback alone, suggesting that VSI does not play a special role in retuning of perceptual representations of speech. Experiment 2 demonstrates that provision of VSI concurrently with NV speech (a simulation of typical real-world experience) facilitates perceptual learning of NV speech, but only when an NV-only repetition of each utterance is presented after the composite NV/VSI form during training. Experiment 3 shows that this more efficient learning of NV speech is probably due to the additional listening effort required to comprehend the utterance when clear feedback is never provided and is not specifically due to the provision of VSI. Our results suggest that rehabilitation after cochlear implantation does not necessarily require naturalistic audiovisual input, but may be most effective when (a) training utterances are relatively intelligible (approximately 85% of words reported correctly during effortful listening), and (b) the individual has the opportunity to map what they know of an utterance's linguistic content onto the degraded form.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".