Acoustic recognition component of an 86000-word speech recognizer
Bibliographic record
Abstract
Recent results obtained with a hidden Markov model (HMM)-based acoustic recognizer using a virtually unlimited vocabulary (86000 words) to perform speaker-dependent isolated-word recognition are described. The task domain of this recognizer is quite general, consisting of paragraphs read from various newspapers, books, and magazines. The results of a comparative acoustic recognition study using various types of HMMs and various amounts of training data (from 700 to about 4000 words) are presented. The models explored include context-dependent allophonic HMMs (including generalized diphone and triphone models with unimodal Gaussian output densities) and context-independent phonemic HMMs (using either unimodal or mixture densities). Experimental results indicate that phonemic HMMs with many components in the mixture output densities provide the highest acoustic recognition accuracy. The acoustic recognition accuracy for a total of about 7000 test words spoken by four male and five female speakers is 82%. Recognition accuracy after application of the language model increases to 92%.>
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".