A constrained joint optimization method for large margin HMM estimation
Bibliographic record
Abstract
In this paper, we propose a new optimization method, i.e., constrained joint optimization method, to solve the minimax optimization problem in large margin estimation (LME) of continuous density hidden Markov model (CDHMM) for speech recognition. First, we mathematically analyze the definition of margin and introduce some theoretically-sound constraints into the minimax optimization to guarantee the boundedness of the margin in LME. Moreover, we propose to solve this constrained minimax optimization problem by using a penalized gradient descent algorithm, where the original objective function, i.e., minimum margin, is approximated by a differentiable function and the new constraints are cast as penalty terms in the objective function. The new method is evaluated in a speaker-independent E-set speech recognition task by using the OGI ISOLET database. Experimental results show that the new constraints are very effective to ensure the convergence of the minimax optimization and the large margin estimation via the resultant optimization method can achieve significant word error rate (WER) reduction over the conventional HMM training methods, such as MLE and MCE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".