Document-based Dirichlet class language model for speech recognition using document-based n-gram events
Bibliographic record
Abstract
We propose a document-based Dirichlet class language model (DDCLM) for speech recognition using document-based n-gram events. In this model, the class is conditioned on the immediate history context and the document, and the word is conditioned on the the class and the document in the original DCLM model. In the DCLM model, the class information was obtained from the (n-1) history words of n-gram events of a training corpus. Here, the model uses the count of the n-grams, which are the number of appearances of the n-grams in the corpus. These counts are the sum of the n-gram counts in different documents where they could appear to describe different topics. Therefore, the n-gram counts of the corpus may not yield the proper class information for the histories. We encounter this problem in the DCLM model and propose a DDCLM model that overcomes the above problem by finding the class information for the document-based history context using the document-based n-gram events. We carried out experiments on a continuous speech recognition (CSR) task using the Wall Street Journal (WSJ) corpus and have seen that the proposed approach shows significant perplexity and word error rate (WER) reductions over the other approach.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".