Solving Large-Margin Hidden Markov Model Estimation via Semidefinite Programming
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Bibliographic record
Abstract
In this paper, we propose to use a new optimization method, i.e., semidefinite programming (SDP), to solve the large-margin estimation (LME) problem of continuous-density hidden Markov model (CDHMM) in speech recognition. First, we introduce a new constraint for LME to guarantee the boundedness of the margin of CDHMM. Second, we show that the LME problem subject to this new constraint can be formulated as an SDP problem under some relaxation conditions. Therefore, it can be solved using many efficient optimization algorithms specially designed for SDP. The new LME/SDP method has been evaluated on a speaker independent E-set speech recognition task using the ISOLET database and a connected digit string recognition task using the TIDIGITS database. Experimental results clearly demonstrate that large-margin estimation via semidefinite programing (LME/SDP) can significantly reduce word error rate (WER) over other existing CDHMM training methods, such as MLE and MCE. It has also been shown that the new SDP-based method largely outperforms the previously proposed LME optimization methods using gradient descent search.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 it