Effects of Hearing Loss on the Voice in Children
Bibliographic record
Abstract
The object of this paper is to report on preliminary acoustic characteristics obtained from a group of 10 to 12 year old males from special institution from Zagreb with more than mild sensorineural hearing losses. The study was structured as an investigation of voice and resonance characteristics of Croatian children with and without sensorineural hearing loss, using sustained phonation of the vowel /a/ which was recorded using a high-quality tape recorder carried out by two voice clinicians. The samples were digitized and analyzed for frequency and spectral characteristics by EZVoice and Bruel & Kjaer Real-time Frequency Analyzer and high quality sound level meter (mouth-to-microphone distance = 30 cm). Differences were observed in perturbation measures; F0 variability; vocal intensity. Spectral deviations were also observed. Discussion focuses on application of these findings by Croatian speech and hearing specialists with the hearing impaired population. Results indicated the following: measures of jitter were significantly elevated in the hearing loss group as compared to the normal controls. A similar result was observed for measures of shimmer. Lack of voice professional's awareness of importance for making pleasant voice quality of hearing-impaired individuals was the initial idea of this study. Patients with hearing losses have been reported to show a wide variety of voice disturbances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".