Discriminative Training Based on the Criterion of Least Phone Competing Tokens for Large Vocabulary Speech Recognition
Bibliographic record
Abstract
In our approach, we first collect a competing token set for each physical HMM from training data. An off-line token collection procedure is used in this work to collect the competing-tokens from word lattices. Then we re-estimate HMM parameters discriminatively to minimize the total number of competing tokens counted in the phone level. The phone token counts are approximated by a sigmoid-based objective function. The GPD algorithm is used to adjust HMM parameters to minimize the objective function. In this work, a merging mechanism and a gradient normalization in the HMM tied-state level are proposed to improve the generalization power of our discriminative training method. The proposed method is evaluated on the resource management (RM) and the switchboard (a 24-hr mini-train set) tasks. Experimental results clearly show that our new discriminative training method achieves significant improvements over our best MLE models in both tasks, namely about 8% and 4.5% relative error rate reduction in RM and switchboard respectively, over the best MLE models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".