A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
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Bibliographic record
Abstract
This paper presents a family of discriminative manifold learning approaches to feature space dimensionality reduction in noise robust automatic speech recognition (ASR). The specific goal of these techniques is to preserve local manifold structure in feature space while at the same time maximizing the separability between classes of feature vectors. In the manifold space, the relationships among the feature vectors are defined using nonlinear kernels. Two separate distance measures are used to characterize the kernels, namely the conventional Euclidean distance and a cosine-correlation based distance. The performance of the proposed techniques is evaluated on two task domains involving noise corrupted utterances of connected digits and read newspaper text. Performance is compared to existing approaches used for feature space transformations, including linear discriminant analysis (LDA) and locality preserving linear projections (LPP). The proposed approaches are found to provide a significant reduction in word error rate (WER) with respect to the more well-known techniques for a variety of noise conditions. Another contribution of the paper is to quantify the interaction between acoustic noise conditions and the shape and size of local neighborhoods which are used in manifold learning to define local relationships among feature vectors. Based on this analysis, a procedure for reducing the impact of varying acoustic conditions on manifold learning is proposed .
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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 | 0.000 | 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 | 0.001 |
| 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 it