Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep neural networks (DNNs) have been successfully applied to a variety of automatic speech recognition (ASR) tasks, both in discriminative feature extraction and hybrid acoustic modeling scenarios. The development of improved loss functions and regularization approaches have resulted in consistent reductions in ASR word error rates (WERs). This paper presents a manifold learning based regularization framework for DNN training. The associated techniques attempt to preserve the underlying low dimensional manifold based relationships amongst speech feature vectors as part of the optimization procedure for estimating network parameters. This is achieved by imposing manifold based locality preserving constraints on the outputs of the network. The techniques are presented in the context of a bottleneck DNN architecture for feature extraction in a tandem configuration. The ASR WER obtained using these networks is evaluated on a speech-in-noise task and compared to that obtained using DNN-bottleneck networks trained without manifold constraints. Index Terms: manifold learning, deep neural networks, speech recognition, tandem feature extraction
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.
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 | 0.000 |
| 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