Combined speech decoders output for phoneme recognition enhancement
Bibliographic record
Abstract
Phoneme recognition is an essential component of any robust speech decoder and has been tackled by many researchers. Speech feature extraction constitutes the front end module of any speech decoder: it plays an essential role and has a strong impact on the recognition performance. The research community is aggressively searching for more powerful solutions which combine the existing feature extraction methods for a better and more reliable information capture from the analog speech signal. In this research work, we propose new approaches to combining phoneme recognizers' output in order to provide better recognition performance and improved robustness with respect to noise and channel distortions. Machine learning tools such as the naive Bayes classifier, decision trees, and support vector machines have been used in the combination of hypotheses. Experiments under different SNR levels have proven that our proposed approach outperforms the two most common feature extraction techniques, namely mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) with cepstral mean subtraction (CMS) and RASTA respectively, for channel normalization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".