The Effects of Intrinsic Acoustic Cues on Categorical Perception in Children with Cochlear Implants
Bibliographic record
Abstract
Many previous studies researched the influence of external cues on speech perception, yet little is known pertaining to the role of intrinsic cues in categorical perception of Mandarin vowels and tones by children with cochlear implants (CI). This study investigated the effects of intrinsic acoustic cues on categorical perception in children with CIs, compared to normal-hearing (NH) children. Categorical perception experiment paradigm was applied to evaluate their identification and discrimination abilities in perceiving /i/-/u/ with static intrinsic formants and Tone 1 (T1)-Tone 2 (T2) with dynamic intrinsic fundamental frequency (F0) contours. Results for the NH group showed that vowel continuum of /i/-/u/ was less categorically perceived than T1-T2 continuum with significantly wider boundary width and less alignment between the discrimination peak and the boundary position. However, a different categorical perception pattern was depicted for the CI group. Specifically, the CI group exhibited less categoricalness in both /i/-/u/ and T1-T2. It suggested that the effects of intrinsic acoustic cues on categorical perception was proved for the normal-hearing children, while not for the hearing-impaired children with cochlear implants. In conclusion, acoustically dynamic cues can facilitate categorical perception of speech in NH children, whereas this effect will be inhibited by difficulties in processing spectral F0 information as in the CI 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".