A comparative study on various confidence measures in large vocabulary speech recognition
Bibliographic record
Abstract
In this paper, we have conducted a comparative study on several confidence measures (CM) for large vocabulary speech recognition. Firstly, we propose a novel high-level CM that is based on the inter-word mutual information (MI). Secondly, we experimentally investigate several popular low-level CM, such as word posterior probabilities, N-best counting, likelihood ratio testing (LRT), etc. Finally, we have studied a simple linear interpolation strategy to combine the best low-level CM with the best high-level CM. All of these CM are examined in two large vocabulary ASR tasks, namely the Switchboard task and a Mandarin dictation task, to verify the recognition errors in baseline recognition systems. Experimental results show: (1) the proposed MI-based CM greatly surpass another existing high-level CM which are based on the LSA technique; (2) among all low-level CM, word posteriori probabilities give the best verification performance; (3) when combining the word posteriori probabilities with the MI-based CM, the equal error rate is reduced from 24.4% to 23.9% in the Switchboard task and from 17.5% to 16.2% in the Mandarin dictation task.
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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 | 0.022 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".