Dynamic sign language and voice recognition for smart home interactive application
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.
Bibliographic record
Abstract
This paper presents a system for recognition of dynamic sign language and voice recognition for smart home interactive applications. We use the Bag-of-Features and a local part model approach for bare hand dynamic gesture recognition from video. We use a dense sampling to extract local 3D multiscale whole-part features. We adopted three-dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. The K-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was conducted by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) methods. As the BOF does not track the order of events we use a multiscale local part model to preserve temporal context. Initial experimental results show a higher level of recognition. A voice recognition system is then used to translate voice commands complementing the hand gesture commands for the human-intuitive control of a personal service android robot for smart home or long-term healthcare environment applications.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it