Children’s Speech Recognition Scores: The Speech Intelligibility Index and Proficiency Factors for Age and Hearing Level
Bibliographic record
Abstract
In Brief Objective: The objective of this study was to predict consonant recognition scores of adults, children, and children with hearing impairment, using the Speech Intelligibility Index (SII). It was hypothesized that an adult-derived transfer function would be insufficient to predict the scores for children, and that transfer functions for normally hearing listeners would be insufficient to predict scores for children with hearing impairment. Proficiency corrections for age and hearing loss were explored. Design: A 21-consonant test of speech recognition was applied across five signal to noise ratios in a forced choice procedure. Four adults (aged 27–32 yrs), 15 children with normal hearing (aged 6.6–16.9 yrs), and 14 children with mild to severe hearing loss (aged 7.5–18 yrs) participated. The SII was computed for each listener and each test condition using the one-third octave band method. Transfer functions were fitted to the data of each group. Results: The adult-derived transfer function over-predicted the children’s scores. Significant increases in prediction accuracy were obtained when the effects of age and hearing loss were incorporated into the transfer function as proficiency factors. Conclusions: The SII could successfully be used to predict speech recognition scores for both adults and children, once the effects of age and hearing loss were included in the development of a transfer function. Specific proficiency factors developed here may not generalize to other data sets. Nonetheless, the results shed light on factors to consider when using the SII to predict children’s speech recognition scores. The objective of this study was to predict consonant recognition scores of adults and children with/without hearing impairment. A consonant recognition test was administered across signal to noise ratios. Three groups of listeners participated: adults and children with normal hearing, and children with hearing loss. The Speech Intelligibility Index was computed for each listener and test condition, and transfer functions were fitted to the data of each group. The adult-derived transfer function over-predicted the children's scores. Significant increases in prediction accuracy were obtained when the effects of age and hearing loss were incorporated into the transfer function as proficiency factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".