Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 it