Design and Evaluation of a Vocalization Activated Assistive Technology for a Child with Dysarthric Cpeech
Bibliographic record
Abstract
Communication disorders affect one in ten Canadians and the incidence is particularly high among those with Cerebral Palsy. A vocalization-activated switch is often explored as an alternative means to communication. However, most commercial speech recognition tools to date have limited capability to accommodate dysarthric speech and thus are often prematurely abandoned. We developed and evaluated a novel vocalization-based access technology as a writing tool for a pediatric participant with cerebral palsy. It consists of a high quality condenser headmic, a custom classifier based on Gaussian Mixture Modeling (GMM) and Mel-frequency Cepstral Coefficients (MFCC) as features. The system was designed to discriminate among five vowel sounds while interfaced to an on-screen keyboard. We used response efficiency theory to assess this technology in terms of goal attainment and satisfaction. The participant’s primary goal to reduce switch activation time was achieved with increased satisfaction and lower physical effort when compared to her previous pathway.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".