Bibliographic record
Abstract
Motivated by large margin classifiers in machine learning, we propose a novel method to estimate a continuous density hidden Markov model (CDHMM) in speech recognition according to the principle of maximizing the minimum multi-class separation margin. The approach is named large margin HMM. First, we show that this type of large margin HMM estimation problem can be formulated as a standard constrained minimax optimization problem. Second, we propose an iterative localized optimization approach to perform the minimax optimization for one model at a time to guarantee that the optimal value of the objective function always exists in the course of model parameter optimization. Then, we show that during each step the optimization can be solved by the GPD (generalized probabilistic descent) algorithm if we approximate the objective function by a differentiable function, such as summation of exponential functions. The large margin HMM-based classifiers are evaluated in a speaker-independent E-set speech recognition task using the OGI ISOLET database. Experimental results show that the large margin HMMs can achieve significant word error rate (WER) reduction over conventional HMM training methods, such as maximum likelihood estimation (MLE) and minimum classification error (MCE) training.
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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 | 0.001 | 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) | 0.003 | 0.002 |
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".