A New Minimum Divergence Approach to Discriminative Training
Bibliographic record
Abstract
We propose to use minimum divergence, where acoustic similarity between HMMs is characterized by Kullback-Leibler divergence, for discriminative training. The MD objective function is defined as a posterior weighted divergence measured over the whole training set. Different from our earlier work, where KLD-based acoustic similarity is pre-computed for all initial models and stays invariant in the optimization procedure, here we propose to jointly optimize the whole variable MD by adjusting HMM parameters since MD is a function of the adjusted HMM parameters. An EBW optimization method is derived to minimize the whole MD objective function. The new MD formulation is evaluated on the TIDIGITS and Switchboard databases. Experimental results show that the new MD yields relative word error rate reductions of 62.1% on TIDIGITS and 8.8% on Switchboard databases when compared with the best ML-trained systems. It is also shown the new MD consistently outperforms other discriminative training criteria, such as MPE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".