Variable frame size for vector quantization and application to speech coding
Bibliographic record
Abstract
L'auteur a accord une licence non exclusive permettant la Bibliothque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par tlcommunication ou par l'Internet, prter, distribuer et vendre des thses partout dans le monde, des fins commerciales ou autres, sur support microforme, papier, lectronique et/ou autres formats.L'auteur conserve la proprit du droit d'auteur et des droits moraux qui protge cette thse.Ni la thse ni des extraits substantiels de celle-ci ne doivent tre imprims ou autrement reproduits sans son autorisation.Conformment la loi canadienne sur la protection de la vie prive, quelques formulaires secondaires ont t enlevs de cette thse.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.My first and foremost expression of gratitude goes to my research advisor, Professor Fabrice Labeau, for his continuous support, encouragement and guidance throughout all the phases of this project.l very much enjoyed the time spent on technical discussions with Prof. Labeau, and l feel that these discussions greatly contributed to a higher quality of the education l received from McGill University.l also consider myself fortunate to have had such high-quality instructors during the courses that l attended as part of this degree.l am very grateful to each and every one of the instructors l had.They made me and all my fellow students work very hard during those semesters; for that, l will always be grateful and will regard them as excellent instructors.Among these instructors, l would especially like to address my thanks to Prof. Douglas O'Shaughnessy and Prof.Richard Rose, who provided me with a very enjoyable introduc
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".