Speech auditory brainstem responses and words-in-noise recognition scores: is there a link?
Bibliographic record
Abstract
Difficulties recognizing speech in the presence of background noise are often associated with school-based learning problems. One could expect that early identification of these listening difficulties could prevent negative impacts on academic success. Unfortunately, the lack of adequate clinical tools makes it impossible to do so before the age of six. With a view to develop a screening tool for listening difficulties in noise using speech evoked ABR, the aim of this study was to investigate a possible correlation between speech auditory brainstem responses and the performance measured at a words-in- noise recognition task. Forty-five normal hearing adults participated in this study and were divided into three groups. Behavioral measures included a speech recognition task in which a list of words was presented without noise for one condition (quiet), and with a competitive white noise for the other condition(noise). Speech-evoked ABR measures followed, using a recorded syllable presented with and without a competitive noise. A different signal-to- noise ratio (SNR) was used for each group in the noise conditions. As expected, lower word recognition scores were noted in the noise compared to quiet conditions. The lowest average score was obtained by the group exposed to the most difficult SNR. Differences were also observed on the electrophysiological components measured with and without the noise, but the changes were not linear. Interpretations about the relationship between brainstem function and speech recognition in noise performance will be presented. This study contributes to a better understanding of the brainstem responses as a quantifiable measure of the neural encoding of speech sounds and furthermore, to allow for early detection of speech recognition problems in noise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".