Regularized constrained maximum likelihood linear regression for speech recognition
Bibliographic record
Abstract
The use of a graph embedding framework is investigated as a regularization technique in the expectation-maximization (EM) algorithm applied to automatic speech recognition (ASR). The technique is motivated by the fact that graph em-beddings of feature vectors have been shown to provide useful characterizations of the underlying manifolds on which these features lie. Incorporating intrinsic graphs that describe these manifolds in the optimization criteria for the EM algorithm has the effect of constraining the solution space in a way that preserves the local structure of the data. Graph embedding based regularization is applied here to estimating parameters in constrained maximum likelihood linear regression (CMLLR) speaker adaptation in continuous density hidden Markov model (CDHMM) based ASR. CMLLR adaptation has been widely used as a maximum likelihood procedure for reducing mismatch between a given HMM model and utterances from an unknown speaker through a linear feature space transformation. However, there is no guarantee that CMLLR transformations will preserve the relationships of the feature vectors along this manifold. It is argued here that graph embedding based regularization will preserve this structure. The impact of this approach on ASR performance is evaluated for unsupervised speaker adaptation on two large vocabulary speech corpora.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".