A 3D Voice-Hearing Simulator Co-Created by Voice Hearers and Researchers: Preliminary Sound Quality Evaluation
Bibliographic record
Abstract
Voice hearing (VH) is a phenomenon that can affect a wide range of people, not just those affected by psychosis, schizophrenia or any mental health trouble. The heard voices can be positive, negative, or anything in between. VH is distinctive in that the voices can both come from inside the head or from the external world (i.e. the sound environment, or the body), resulting in a sense of sound externalization. Improper mental health services or social support are often reported by voice hearers. One hypothesis is that service providers lack empathy and appropriate knowledge about the experience of voice hearers, potentially leading to a reduced therapeutic alliance. To address this, a 3D voice-hearing simulator (3DV) was created within a participatory research paradigm with a multidisciplinary team. The main objective being the creation of an experiential knowledge sharing tool for the training of future psychiatrists or social workers. Simple binaural sound reproduction was used to recreate the sense of externalization. A validation of its effect was performed using quantitative and qualitative evaluation on social work students. Preliminary results suggest that the 3DV was efficient in sharing the VH experience for both voice hearers and participants. Hence, one of the strong successes of the project was in the inclusion of voice hearers as non-traditional research collaborators with experiential knowledge. However, in terms of limitation, it was difficult for participants, considered as naive listeners with respect to audio technologies, to distinguish the sound quality of different 3DV versions (binaural, stereophonic, and binaural based on ambisonics). This is possibly due to the strong and impactful nature of the 3DV and the words per se. To improve the immersion of 3DV, full immersive and interactive binaural audio with head-tracking or augmented reality may be necessary. This project received an ethics certificate from UQAM and financial support from FRQ Audace.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".