Parallel recognizer algorithm for automatic speech recognition
Bibliographic record
Abstract
Research in Automatic Speech Recognition (ASR) has been very intense in recent years with focus given to accuracy and speed issues. To achieve good accuracy, the employed techniques usually rely on heavy computations. Agbago and Barrière [2] earlier defined a Three-Stage Architecture (TSA) framework for ASR composed of (1) pre-processing stage, (2) phomene recognition stage, and (3) natural language post-processor stage. Within that TSA framework, our present focus is to improve the speed of Stage 2 which looks specifically at the comparison of low level speech units. It is different from several sytems that include HMM processes in this Stage (e.g. Shawn's [5]). We present a new algorithm called Parallel Recognizer that is 320 times faster than a standard Two-Level Dynamic programming (TLDP) [3]. In comparison, working on speed at low-level, Nkagawa [4] got a reduction of 4 to 6 the time needed to compute local distances in the improved DG algorithm of Sakoe [6].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".