Binaural speech recognition in continuous and intermittent noises in people with hearing loss
Bibliographic record
Abstract
Multiple tests have been developed to quantify speech recognition in noise, but the characteristics of the masking noise vary significantly across tests and can considerably impact performance and clinical interpretation. Using the HINT, speech perception in 24 young adults with normal hearing was assessed using both the standard masker (a continuous speech-spectrum noise) and an intermittent version of the masker at an ON-OFF rate of 16 Hz. Intermittency helps in extracting speech from the "quiet" segments of the noise. Speech recognition thresholds for frontal speech were better in the intermittent than the continuous noise by an amount of 13 and 10 dB for noise maskers located in front or side, respectively, The average difference in thresholds between the noise front and side conditions, called the binaural advantage, was 4.7 and 8.4 dB for the intermittent and continuous noises, respectively. Data collected with people presenting different hearing loss profiles also show binaural and intermittency advantages, but to a lesser degree. Considering that people encounter a wide a range of fluctuating noises in daily life, these results motivate adding an intermittent noise condition to the HINT protocol to better reflect the challenges faced by individuals with hearing loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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".