Linear Regression Based Acoustic Adaptation for the Subspace Gaussian Mixture Model
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Bibliographic record
Abstract
This paper presents a study of two acoustic speaker adaptation techniques applied in the context of the subspace Gaussian mixture model (SGMM) for automatic speech recognition (ASR). First, a model space linear regression based approach is presented for adaptation of SGMM state projection vectors and is referred to as subspace vector adaptation (SVA). Second, an easy to implement realization of constrained maximum likelihood linear regression (CMLLR) is presented for feature space adaptation in the SGMM. Numerically stable procedures for row-by-row estimation of the regression based transformation matrices are presented for both SVA and CMLLR adaptation. These approaches are applied to SGMM models that are estimated using speaker adaptive training (SAT), a technique for estimating more compact speaker independent acoustic models. Unsupervised speaker adaptation performance is evaluated on conversational and read speech task domains and compared to unsupervised adaptation performance obtained using the hidden Markov model-Gaussian mixture model (HMM-GMM) in ASR. It is shown that the feature space and model space adaptation approaches applied to the SGMM provide complementary reductions in word error rate (WER) and provide lower WERs than that obtained using CMLLR adaptation for the HMM-GMM.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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