A syllabic-filler-based continuous speech recognizer for unlimited vocabulary
Bibliographic record
Abstract
Most continuous speech recognition systems make the assumption that the input speech to process is limited to correspond to a text from a given dictionary, while fluent speech is, in fact, characterized by the addition of out-of-dictionary words, hesitations and interrupted words. The authors describe an unlimited vocabulary continuous speech recognizer based on new-word detection and transcription. In the system the words from a pre-defined dictionary as well as the extraneous speech (unknown words) use simply the same context-dependent phoneme hidden Markov models (HMMs) trained on data including only known words. The distinction between dictionary words and unknown words is made during the search by the addition of lexical fillers in the lexical tree used by the two-pass Viterbi type algorithm of the scoring method as well as by modifying the language models. A performance comparison is given for phonemic as well as syllabic fillers.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".