New Feature Extraction Approaches Based on Spatial Points for Visual-Only Lip-Reading
Bibliographic record
Abstract
The act of speaking takes place as a result of the joint use of both the senses of vision and hearing. The visual senses of the event of speech play an important role in lip-reading, especially when the sound is distorted or inaccessible. Visual-only-based lip-reading is a more difficult problem than audio-image-based lip-reading problems. In this study, three new spatial feature approaches to visual-only lip-reading are presented. To test the proposed feature extraction approaches, three datasets named AVLetters2 consisting of letters, AVDigits consisting of digits, and AVLetAVDig consisting of a combination of these two were used. First of all, the facial elements and lips were separated and the lip borders were marked with 20 points. Then, based on these spatial points, feature vectors were obtained with the feature approaches named Symmetric Euclidean Distance (SED), Central Euclidean Distance (CED), and Triple Points Angles (TPA). Extracted feature vectors were given to the CNN-LSTM network and 26 characters and 10 digits were tried to be estimated. As a result of the findings, the best success results for AVLetters2, AVDigits, and AVLetAVDig datasets were obtained by the SED+CNN+LSTM method as 53.2, 81.6, 59.8, respectively. When compared with the studies in the literature on the same data set, it was seen that very high and successful results were obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".