Detection of ambiguous portions of signal corresponding to OOV words or misrecognized portions of input
Bibliographic record
Abstract
One of the key problems for large vocabulary ASR is the detection of unknown or misrecognized portions of the input. The paper presents results obtained using a local rejection algorithm. The algorithm is derived from the two pass recognition algorithm by H. Murveit et al. (1993) and is used to detect misrecognized portions based on the number per frame of active words during the second pass. The hypothesis underlying the algorithm is that recognition on unexpected data, i.e. noise or out of vocabulary (OOV) words, is likely to result in activation of more words, since no word matches the data well; on the other hand, when the match is good, fewer words should be active. The algorithm was tried on part of the WSJ 5K November 1993 test, in which there were no OOV words (3370 words in total) and on the digit strings only Macrophone data (14686 words of which 895 were OOV). The results obtained indicate that our approach is promising, both for the detection of OOV words and misrecognized portions of the input. It may provide the base on which to build tools for dealing with these phenomena. These tools might include dialogue mechanisms based on the list of activated words corresponding to a rejected portion, display mechanisms such as reverse video or rescoring schemes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".