A compact semidefinite programming (SDP) formulation for large margin estimation of HMMS in speech recognition
Bibliographic record
Abstract
In this paper, we study a new semidefinite programming (SDP) formulation to improve optimization efficiency for large margin estimation (LME) of HMMs in speech recognition. We re-formulate the same LME problem as smaller-scale SDP problems to speed up the SDP-based LME training, especially for large model sets. In the new formulation, instead of building the SDP problem from a single huge variable matrix, we consider to formulate the SDP problem based on many small independent variable matrices, each of which is built separately from a Gaussian mean vector. Moreover, we propose to further decompose feature vectors and Gaussian mean vectors according to static, delta and accelerate components to build even more compact variable matrices. This method can significantly reduce the total number of free variables and result in much smaller SDP problem even for the same model set. The proposed new LME/SDP methods have been evaluated on a connected digit string recognition task using the TIDIGITS database. Experimental results show that it can significantly improve optimization efficiency (about 30-50 times faster for large model sets) and meanwhile it can provide slightly better optimization accuracy and recognition performance than our previous SDP formulation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".