Bibliographic record
Abstract
We propose to use a stochastic segmental intensity model independent of the HMM model in INRS's large vocabulary continuous speech recognizer. First, we examine how to insert this model into the search algorithm without violating the optimality constraints of this algorithm. Second, we propose and test the performance of four different intensity models. The training and testing of the models is done on a studio quality speaker-dependent speech corpus. The first model is a Gaussian mixture phone intensity model independent of the phonemic context. The second model is a Gaussian mixture phone intensity model dependent on the right or left phoneme context. The third model is a Gaussian mixture intensity model based on the variation of intensity within a diphone. Finally, the last model consists of a stochastic silence-speech detector. Performance comparisons show that the best model uses Gaussian mixture of the variation of intensity within a diphone (third model). This model improves the percentage of word recognition from 89.58% (no intensity modeling) to 90.92%.
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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 | 0.000 | 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) | 0.000 | 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".