Localization and Speech-in-Noise Performance in a Virtual Reality Test Environment: A Pilot Study of Adults With Single-Sided Deafness Using a Cochlear Implant
Bibliographic record
Abstract
PURPOSE: The purpose of this exploratory study was to (a) construct a virtual reality (VR) test environment to measure speech recognition in noise (SIN) and localization, and (b) use the VR test environment to establish degree of binaural hearing benefit among a small number of adults with single-sided deafness (SSD) using a cochlear implant (CI). METHOD: This pilot study included five adults implanted for SSD. The test environment was composed of an eight-speaker array that delivered restaurant noise and Institute of Electrical and Electronics Engineers sentences. VR head-mounted display goggles delivered a video recording of a busy restaurant. Participants completed SIN and localization in two conditions: (a) normal-hearing ear and a CI on the contralateral SSD side (CI-ON) and (b) normal-hearing ear and unaided on the contralateral SSD side (CI-OFF). RESULTS: Overall, CI benefits for SIN and localization within the VR test environment were improved for some participants, although not all. CI benefit for SIN and localization was dependent on speaker location. CONCLUSIONS: VR test environments present new opportunities for studying SIN and localization abilities in participants with CIs. This pilot study shows that, within a VR test environment, degree of CI benefit among SSD participants for SIN and localization varies across speaker location and across participants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".