Integration of Multiple Feature Sets for Reducing Ambiguity in ASR
Bibliographic record
Abstract
The main goal of this paper is to investigate the feasibility of exploiting the invariance properties associated with articulatory based acoustic features to reduce ambiguity in ASR search. A multivalued phonological feature set defined by King and Taylor is used along with a time delay neural network implementation of phonological feature detectors to produce eight independent phonological feature streams (S. King and P. Taylor, 2000). Hidden Markov models (HMMs) defined over these phonological feature streams are combined with HMMs defined over spectral energy based mel frequency cepstrum coefficient (MFCC) acoustic features through a lattice re-scoring procedure. It is shown that significant improvements in phone recognition accuracy are obtained for this combined system relative to phone accuracy obtained for MFCC based HMMs alone. A study is also performed to analyze the effects of uncertainty in phonological feature detection.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".