Adaptive Syllable Training Improves Phoneme Identification in Older Listeners with and without Hearing Loss
Bibliographic record
Abstract
Acoustic-phonetic speech training mitigates confusion between consonants and improves phoneme identification in noise. A novel training paradigm addressed two principles of perceptual learning. First, training benefits are often specific to the trained material; therefore, stimulus variability was reduced by training small sets of phonetically similar consonant-vowel-consonant syllables. Second, the training is most efficient at an optimal difficulty level; accordingly, the noise level was adapted to the participant's competency. Fifty-two adults aged between sixty and ninety years with normal hearing or moderate hearing loss participated in five training sessions within two weeks. Training sets of phonetically similar syllables contained voiced and voiceless stop and fricative consonants, as well as voiced nasals and liquids. Listeners identified consonants at the onset or the coda syllable position by matching the syllables with their orthographic equivalent within a closed set of three alternative symbols. The noise level was adjusted in a staircase procedure. Pre-post-training benefits were quantified as increased accuracy and a decrease in the required signal-to-noise ratio (SNR) and analyzed with regard to the stimulus sets and the participant's hearing abilities. The adaptive training was feasible for older adults with various degrees of hearing loss. Normal-hearing listeners performed with high accuracy at lower SNR after the training. Participants with hearing loss improved consonant accuracy but still required a high SNR. Phoneme identification improved for all stimulus sets. However, syllables within a set required noticeably different SNRs. Most significant gains occurred for voiced and voiceless stop and (af)fricative consonants. The training was beneficial for difficult consonants, but the easiest to identify consonants improved most prominently. The training enabled older listeners with different capabilities to train and improve at an individual 'edge of competence'.
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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 | 0.002 | 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 | 0.001 |
| 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".