Noise aware manifold learning for robust speech recognition
Bibliographic record
Abstract
This paper considers the application of discriminative manifold learning approaches in feature analysis for automatic speech recognition (ASR). The issue of manifold learning is addressed for feature space dimensionality reduction in domains involving noise corrupted speech. The locality preserving discriminant analysis (LPDA) approach to manifold learning is investigated. This class of techniques exploits the assumption that there is a structural relationship among data vectors which can be maintained by preserving the local relationships among the transformed data vectors. The paper presents a procedure for reducing the impact of varying acoustic conditions on manifold learning. Noise aware manifold learning (NAML) is described as an approach for exploiting estimated background characteristics to define the size of the local neighborhoods used for LPDA feature space transformations. It is shown that NAML significantly reduces the speech recognition WER in a noisy speech recognition task over LPDA, particularly at low signal-to-noise ratios.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".