Facilitating open vocabulary spoken term detection using a multiple pass hybrid search algorithm
Bibliographic record
Abstract
This paper presents an efficient approach to spoken term detection (STD) from unstructured audio recordings using word lattices generated off-line from an automatic speech recognition (ASR) system. The approach facilitates open vocabulary STD and focuses specifically on reducing the difference between detection performance obtained for within-vocabulary (IV) and out-of-vocabulary (OOV) search terms. Improved OOV detection performance is obtained by using a two-pass search procedure. Candidate audio segments are retrieved from an index of word lattice paths in the first pass. Locations of OOV search terms are detected in the second pass from a constrained alignment of phonemic expansions of the query terms with phoneme sequences obtained from acoustic segments using an unconstrained neural network based phone decoder. It is found that the combination of first pass segment retrieval and second pass term verification significantly increases STD performance for OOV query terms with no increase in search time for utterances taken from a lecture speech domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".