Voice emotion recognition by Mandarin‐speaking pediatric cochlear implant users in Taiwan
Bibliographic record
Abstract
Abstract Objectives To explore the effects of obligatory lexical tone learning on speech emotion recognition and the cross‐culture differences between United States and Taiwan for speech emotion understanding in children with cochlear implant. Methods This cohort study enrolled 60 cochlear‐implanted (cCI) Mandarin‐speaking, school‐aged children who underwent cochlear implantation before 5 years of age and 53 normal‐hearing children (cNH) in Taiwan. The emotion recognition and the sensitivity of fundamental frequency ( F 0) changes for those school‐aged cNH and cCI (6–17 years old) were examined in a tertiary referred center. Results The mean emotion recognition score of the cNH group was significantly better than the cCI. Female speakers' vocal emotions are more easily to be recognized than male speakers' emotion. There was a significant effect of age at test on voice recognition performance. The average score of cCI with full‐spectrum speech was close to the average score of cNH with eight‐channel narrowband vocoder speech. The average performance of voice emotion recognition across speakers for cCI could be predicted by their sensitivity to changes in F 0. Conclusions Better pitch discrimination ability comes with better voice emotion recognition for Mandarin‐speaking cCI. Besides the F 0 cues, cCI are likely to adapt their voice emotion recognition by relying more on secondary cues such as intensity and duration. Although cross‐culture differences exist for the acoustic features of voice emotion, Mandarin‐speaking cCI and their English‐speaking cCI peer expressed a positive effect for age at test on emotion recognition, suggesting the learning effect and brain plasticity. Therefore, further device/processor development to improve presentation of pitch information and more rehabilitative efforts are needed to improve the transmission and perception of voice emotion in Mandarin. Level of evidence 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".