Mandarin Tone Production in Prelingually Deaf Adults With Hearing Aids and Cochlear Implants
Bibliographic record
Abstract
PURPOSE: 0], Slope, and Curve), characterize tonal production and examines the effects of rhyme complexity and hearing device type. METHOD: Seventy-four participants (18 CI users, 26 HA users, and 30 normal-hearing [NH] controls) completed tone production tasks involving 48 monosyllabic words categorized by rhyme complexity (simple, open, nasal). Acoustic analyses were conducted using linear mixed models to examine the influences of device type, rhyme complexity, and tone type on duration and parabola parameters. RESULTS: No significant differences were found between CI and HA users for parabola parameters; both CI participants and HA users produced Mandarin tones with pitch patterns comparable to those of NH individuals but relied on significantly longer durations. Nasal rhymes posed the greatest challenges for deaf individuals, often resulting in longer production durations and larger duration differences from other rhyme types. CONCLUSIONS: Prelingually deaf adults could effectively differentiate tones using parabola parameters, while also tending to extend durations in their production. Rhyme complexity and hearing device significantly impact tone production. These findings provide critical insights into speech rehabilitation for hearing-impaired populations. Further research on diverse contexts is recommended to enhance intervention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".