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Record W2006802181 · doi:10.1121/1.3385277

Developing vowel mappings for an interactive voice synthesis system controlled by hand motions.

2010· article· en· W2006802181 on OpenAlexaffabout
Karl I. Nordstrom, Sidney Fels, Cameron D. Hassall, Bob Pritchard

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiphthongFormantVowelComputer scienceGestureAcousticsSpeech recognitionArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This study investigates vowel mappings for a voice synthesizer controlled by hand gestures for artistic performance. The vowel targets are on a horizontal plane navigated by the movement of the right hand in front of the performer. Two vowel mappings were explored. In one mapping, the vowels were evenly distributed in a circle to make the vowel targets easier for the performer to find. In the other mapping, the vowels were arranged according to the F2 versus F1 space. Linear hand motions were then made through the vowel space while plotting the formant trajectories. The evenly distributed mapping resulted in formant trajectories that were not monotonic; the F1 and F2 pitch contours varied up and down as the hand carried out the linear motions. This had the unintended result of producing multiple diphthongs. In contrast, the F2 versus F1 mapping enabled the performer to create monotonic formant trajectories and the perception of a single diphthong. The performer found it easier to speak and sing through the system when a single linear hand motion resulted in a single diphthong. [This project was supported by Canada Council for the Arts, Natural Sciences and Engineering Council of Canada, and Media and Graphics Interdisciplinary Centre.]

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.254
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2010
Admission routes2
Has abstractyes

Explore more

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