French large vocabulary recognition with cross-word phonology transducers
Bibliographic record
Abstract
Although finite-state transducers have been widely used in linguistics, their application to speech recognition has begun only recently (M. Mohri, 1997). We describe our implementation of French large vocabulary recognition based on transducers, and how we take advantage of this approach to integrate automatic pronunciation rules and cross-word phenomena such as French "liaison". We also show that a simple, single-level Viterbi algorithm can efficiently decode speech recognition transducers and handle cross-word context models and cross-word phonological rules. In our experiments we compared network size, error rate and decoding speed of our transducer based recognizer against a baseline HTK recognizer, on a large vocabulary French dictation task. Transducers reduced search time by a factor of 25 compared to our HTK recognizer. We also examined the effect of automated pronunciation rules, and their combination with crossword phonological rules that control "liaison". We obtained a 23% relative reduction in the word error rate on a 5000 word task.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".