Bibliographic record
Abstract
The objective of this study was to predict the consonant-recognition scores of adults, normal-hearing children, and children with hearing loss, using the Speech Intelligibility Index (SII). The SII can predict speech-perception scores by applying an audibility/intelligibility transfer function (AITF). AITFs from listeners with normal hearing may overestimate performance for listeners with hearing loss. Proficiency and/or desensitization corrections, however, have been shown to adjust for this overestimation. Age-related corrections for reduced proficiency have been proposed for use with the elderly. Children require different audibility levels from adults to achieve similar performance. However, the use of age-related proficiency factors has not been investigated in the pediatric population. It was hypothesized that age-related and hearing-related proficiency factors would be required to predict scores successfully across subject groups. We applied a 21-consonant test of speech recognition across five SNRs. Participants were 4 normal-hearing adults, 15 normal-hearing children (ages: 6.6 to 16.9 years, mean = 9.7), and 14 children with hearing loss (ages: 7.5 to 18, mean = 12.5; PTA: 22–72 dB, mean = 50 dB HL). Listeners with hearing impairment completed the TEN test to determine the presence or absence of dead regions in the cochlea. A computer-controlled, 21-consonant, nonsense-bisyllable, speech-recognition task was presented to each subject in quiet and at four SNRs for each of two talkers and two repetitions. The masker was gaussian noise, spectrally shaped to the long-term average speech spectrum of each talker. Stimuli were processed through a custom hearing aid simulator and presented monaurally through an ER3A insert earphone. Presentation stimuli were the DSL[i/o] target levels for 60-dB-SPL speech, using a loudness-normalization strategy. The audibility per listening condition was quantified using the standard SII, taking into account individual real-ear coupler differences for all children. Non-linear age-related and hearing-related proficiency factors were required to predict the scores of all participants accurately. The resulting functions described 94% to 97% of the variance in the speech-recognition scores for the three experimental groups. In summary, the SII could be used successfully to predict speech-recognition scores for both adults and children when an age- and hearing-dependent transfer function was applied. Clinical implications include the following: Children require a greater SNR than adults to achieve the same levels of speech recognition. This is consistent with the pediatric literature. Adult-derived transfer functions should not be used to predict speech-recognition scores for children from either the Articulation Index (AI) or the SII. Listeners with hearing loss require a higher SNR than listeners with normal hearing. This is consistent with the AI/SII literature.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".