Modeling acoustic-phonetic detail in an HMM-based large vocabulary speech recognizer
Why this work is in the frame
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Bibliographic record
Abstract
The acoustic recognizer of the INRS-Telecommunications 60000-word-vocabulary isolated-word recognition system is discussed. The task of the acoustic recognizer is to generate a list of word hypotheses and their likelihoods based on the acoustic data for each input word. Two sets of experiments are reported in which such knowledge is incorporated into the hidden Markov models (HMMs) used during recognition. In the first set, vowel duration properties are used in the HMMs. In the second set, word-initial and word-final stop consonants are modeled as a sequence of context-dependent subphonemes. The performance of the recognizer is significantly improved by appropriate utilization of the context-dependent vowel-duration information and the context-dependent microsegmental properties of stop consonants. >
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it