A robust compensation strategy for extraneous acoustic variations in spontaneous speech recognition
Why this work is in the frame
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Bibliographic record
Abstract
We propose a robust compensation strategy to deal effectively with extraneous acoustic variations for spontaneous speech recognition. This strategy extends speaker adaptive training, and uses hidden Markov models (HMM) parameter transformations to normalize the extraneous variations in the training data according to a set of predefined conditions. A "compact" model and the associated prior probability density functions (PDFs) of transformation parameters are estimated using the maximum likelihood criterion. In the testing phase, the generic model and the prior PDFs are used to search for the unknown word sequence based on Bayesian prediction classification (BPC). The proposed strategy is evaluated in the switchboard task, and is used to deal with three types of extraneous variations and mismatch in conversational speech recognition: pronunciation variations, inter-speaker variability, and telephone handset mismatch. Experimental results show that moderate word error rate reduction is achieved in comparison with a well-trained baseline HMM system under identical experimental conditions.
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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 | 0.001 |
| 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 | 0.000 |
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