A study of using locality preserving projections for feature extraction in speech recognition
Bibliographic record
Abstract
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality reduction algorithm which can be trained and applied as a linear projection to ASR features. Conventional manifold based dimensionality reduction algorithms are generally restricted to batch mode implementation and it is difficult in practice to apply them to unseen data. It is argued that LPP can model feature vectors that are assumed to lie on a nonlinear embedding subspace by preserving local relations among input features, so it has a potential advantage over conventional linear dimensionality reduction algorithms like principal components analysis (PCA) and linear discriminant analysis (LDA). Experimental results obtained on the Resource Management (RM) data set showed that when LPP based dimensionality reduction was applied in the context of mel frequency cepstrum coefficient (MFCC) based feature analysis, a significant reduction of word error rate (WER) was obtained with respect to standard MFCC features.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".