Application of a locality preserving discriminant analysis approach to ASR
Bibliographic record
Abstract
This paper presents a comparison of three techniques for dimensionally reduction in feature analysis for automatic speech recognition (ASR). All three approaches estimate a linear transformation that is applied to concatenated log spectral features and provide a mechanism for efficient modeling of spectral dynamics in ASR. The goal of the paper is to investigate the effectiveness of a discriminative approach for estimating these feature space transformations which is based on the assumption that speech features lie on a non-linear manifold. This approach is referred to as locality preserving discriminant analysis (LPDA) and is based on the principle of preserving local within-class relationships in this non-linear space while at the same time maximizing separability between classes. This approach was compared to two well known approaches for dimensionality reduction, linear discriminant analysis (LDA) and locality preserving linear projection (LPP), on the Aurora 2 speech in noise task. The LPDA approach was found to provide a significant reduction in WER with respect to the other techniques for most noise types and signal-to-noise ratios (SNRs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".