Importance of high frequency audibility on speech recognition with and without visual cues in listeners with normal hearing
Bibliographic record
Abstract
Purpose: To study the impact of visual cues, speech materials and age on the frequency bandwidth necessary for optimizing speech recognition performance in listeners with normal hearing.\nMethod: Speech recognition abilities of adults and children with normal hearing were assessed using three speech perception tests that were low-pass (LP) filtered and presented in quiet and noise. The speech materials included the Multimodal Lexical Sentence Test (MLST) that was presented in auditory-only and auditory-visual modalities for the purpose of determining the listener's visual benefit. In addition, The University of Western Ontario Plurals Test (UWO) assessed listeners' ability to detect high frequency acoustic information (e.g., /s/ and /z/) in isolated words and The Maryland CNC test that assessed speech recognition performance using isolated single words. Speech recognition performance was calculated as percent correct and was compared across groups (children and adults), tests (MLST, UWO, and CNC) and conditions (quiet and noise).\nResults: Statistical analyses revealed a number of significant findings. The effect of visual cues was significant in adults and children. The type of speech material had significant impact on the frequency bandwidth required for adults and children to optimize speech recognition performance. The children required significantly more bandwidth to optimize performance than adults across speech perception tests and conditions of quiet and noise. Adults and children required significantly more bandwidth in noise than in quiet across speech perception tests.\nConclusion: The results suggest that children and adults require significantly less bandwidth for optimizing speech recognition performance when assessed using sentence materials which provide visual cues. Children, however, showed less benefit from visual cues in the noise condition than adults. The amount of bandwidth required by both groups decreased as a function of the speech material. In other words, the more ecologically valid the speech material (e.g., sentences with visual cues versus single isolated words), the less bandwidth was required for optimizing performance. In all, the optimal bandwidth (except for the noise condition of the UWO test) is achievable with current amplification schemes.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".