A new research environment for speech testing using hearing-device processing algorithms
Bibliographic record
Abstract
A common complaint among subjects with hearing loss is the difficulty of understanding speech in the presence of background noise. Speech testing is an integral part of a clinical assessment of hearing loss, complementing the pure tone audiogram and providing useful information regarding speech tolerance and recognition ability as well as helpful cues for hearing-aid fitting. Among other indicators, speech tests are used to determine the speech reception threshold (SRT) defined as the lowest level at which speech can be correctly identified at least 50 percent of the time. The use of such tests is very popular in research as it allows an experimental manipulation of listening conditions in order to study their effects on speech perception with respect to a baseline condition. In this work, we present a new software-based speech testing environment developed in MATLAB in order to facilitate research in speech perception. It includes a graphical user interface which, in addition to the controls required to run the speech test, provides parameters to specify a subject’s hearing profile and a range of listening and processing conditions. It makes use of head-related transfer functions (HRTFs) to simulate different spatial configurations for binaural listening, and a hearing-device simulator to perform a variety of signal-processing conditions commonly found in hearing devices. The system’s design and feature set will be described along with some experimental results collected using the speech material from the Hearing in Noise Test (HINT).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".