MAPPING A CONTINUOUS VOWEL SPACE TO HAND GESTURES
Bibliographic record
Abstract
Converting hand gestures to speech sounds has been proved to be successful in Glove Talk II [Fels and Hinton, IEEE Transactions on Neural Networks, 8(5), (1997), 977]. This work mapped hand gestures to English speech sounds through an adaptive interface including gloves, space trackers and a foot-pedal. A set of hand gestures were designed and each gesture corresponded to one English segment, and apparently, users had difficulties in producing diphthongs with a natural transition. The present study aims to develop a more intuitive and compact, single-handed user interface which converts hand movements directly to a continuous formant space to generate English vowels through a formant based speech synthesizer. We have collected kinematic glove data of two participants using Cyberglove corresponding to wrist movements (up-down) and finger abduction (sideways) for 8 different English vowels as well as diphthongs. We employed a variety of deep neural networks, with varying hyperparameters, mapping the finger and wrist movements to the continuous vowel quadrilateral formant space (F1 and F2) and analysed the performance of these networks. Results demonstrated that our system achieved successful continuous mapping of one hand movements to the formant space, thereby generating English vowels accurately from a variety of hand gestures, and also showed the prospect of producing vowels of other languages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".