Topic n-gram count language model adaptation for speech recognition
Bibliographic record
Abstract
We introduce novel language model (LM) adaptation approaches using the latent Dirichlet allocation (LDA) model. Observed n-grams in the training set are assigned to topics using soft and hard clustering. In soft clustering, each n-gram is assigned to topics such that the total count of that n-gram for all topics is equal to the global count of that n-gram in the training set. Here, the normalized topic weights of the n-gram are multiplied by the global n-gram count to form the topic n-gram count for the respective topics. In hard clustering, each n-gram is assigned to a single topic with the maximum fraction of the global n-gram count for the corresponding topic. Here, the topic is selected using the maximum topic weight for the n-gram. The topic n-gram count LMs are created using the respective topic n-gram counts and adapted by using the topic weights of a development test set. We compute the average of the confidence measures: the probability of word given topic and the probability of topic given word. The average is taken over the words in the n-grams and the development test set to form the topic weights of the n-grams and the development test set respectively. Our approaches show better performance over some traditional approaches using the WSJ corpus.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".