Age-related changes in talker recognition with reduced spectral cues
Bibliographic record
Abstract
Temporal information provided by cochlear implants enables successful speech perception in quiet, but limited spectral information precludes comparable success in voice perception. Talker identification and speech decoding by young hearing children (5-7 yr), older hearing children (10-12 yr), and hearing adults were examined by means of vocoder simulations of cochlear implant processing. In Experiment 1, listeners heard vocoder simulations of sentences from a man, woman, and girl and were required to identify the talker from a closed set. Younger children identified talkers more poorly than older listeners, but all age groups showed similar benefit from increased spectral information. In Experiment 2, children and adults provided verbatim repetition of vocoded sentences from the same talkers. The youngest children had more difficulty than older listeners, but all age groups showed comparable benefit from increasing spectral resolution. At comparable levels of spectral degradation, performance on the open-set task of speech decoding was considerably more accurate than on the closed-set task of talker identification. Hearing children's ability to identify talkers and decode speech from spectrally degraded material sheds light on the difficulty of these domains for child implant users.
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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) | 0.002 | 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".