Processing of Acoustic Information in Lexical Tone Production and Perception by Pediatric Cochlear Implant Recipients
Bibliographic record
Abstract
Purpose – This study examined the utilization of multiple types of acoustic information in lexical tone production and perception by pediatric cochlear implant (CI) recipients who are native speakers of Mandarin Chinese. Method – Lexical tones were recorded from CI recipients and their peers with normal hearing (NH). Each participant was asked to produce a disyllabic word, yan jing, with which the first syllable was pronounced as Tone 3 (a low dipping tone) while the second syllable was pronounced as Tone 1 (a high level tone, meaning ‘eyes’) or as Tone 4 (a high falling tone, meaning ‘eyeglasses’). In addition, a parametric manipulation in fundamental frequency (F0) and duration of Tones 1 and 4 used in a lexical tone recognition task in Peng et al. (2017) was adopted to evaluate the perceptual reliance on each dimension. Results – Mixed-effect analyses of duration, intensity, and F0 cues revealed that NH children focused exclusively on marking distinct F0 contours, while CI participants shortened Tone 4 or prolonged Tone 1 to enhance their contrast. In line with these production strategies, NH children relied primarily on F0 cues to identify the two tones, whereas CI children showed greater reliance on duration cues. Moreover, CI participants who placed greater perceptual weight on duration cues also tended to exhibit smaller changes in their F0 production. Conclusions – Pediatric CI recipients appear to contrast the secondary acoustic dimension (duration) in addition to F0 contours for both lexical tone production and perception. These findings suggest that perception and production strategies of lexical tones are well coupled in this pediatric CIs population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 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".