Reconfiguration of speech recognizers through layered-grammar structure to provide ease of navigation and recognition accuracy in speech-web.
Bibliographic record
Abstract
Developing speech interfaces to large knowledge bases is a new and challenging problem. There is a need for a solution to provide access to large knowledge bases and high recognition accuracy. A partial solution to this problem is to distribute the knowledge base into a network of speech-accessible units of knowledge. But as the number of such units increases the recognition accuracy decreases and navigation among these units becomes difficult. In this thesis, a new technique is investigated. The new technique is based on a layered grammar structure and modification of the unit's input language to provide high recognition accuracy with ease of navigation among units. This technique is a step towards a solution for high recognition accuracy and distribution transparency with ease of navigation for large knowledge bases. A prototype has been implemented to demonstrate the efficiency of the layered grammar based approach. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .Q74. Source: Masters Abstracts International, Volume: 40-03, page: 0727. Adviser: Richard Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".