MétaCan
Menu
Back to cohort
Record W4225125727 · doi:10.1145/3491102.3501871

The Sound of Hallucinations: Toward a more convincing emulation of internalized voices

2022· article· en· W4225125727 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulationHuman–computer interactionComputer scienceSet (abstract data type)AvatarContext (archaeology)UsabilityMultimediaPsychology

Abstract

fetched live from OpenAlex

The need to generate convincing simulation of voices often arises in the context of avatar therapy, a treatment approach for disorders such as schizophrenia. This treatment involves patients interacting with simulations of the entity they imagine to be responsible for the voices they hear, for which there is often no external reference available. However, in such scenarios, there is little knowledge of how to design and reproduce these voices in a convincing manner. Existing voice manipulation interfaces are often complex to use, and highly limited in their ability to modify vocal characteristics beyond small adjustments. To address these challenges, we designed a framework that allows users to explore and select from a large set of voices, and thereafter manipulate the voice(s) to converge towards an effective match for one they have in mind. We demonstrated both the usability and superior performance of this system compared to existing voice manipulation interfaces.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.335
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it