HMM-based speech recognition using state-dependent, linear transforms on Mel-warped DFT features
Bibliographic record
Abstract
We investigate the interactions of front-end feature extraction and back-end classification techniques in HMM based speech recognizer. This work concentrates on finding the optimal linear transformation of Mel-warped short-time DFT information according to the minimum classification error criterion. These transformations, along with the HMM parameters, are automatically trained using the gradient descent method to minimize a measure of overall empirical error count. The discriminatively derived state-dependent transformations on the DFT data are then combined with their first time derivatives to produce a basic feature set. Experimental results show that Mel-warped DFT features, subject to appropriate transformation in a state-dependent manner, are more effective than the Mel-frequency cepstral coefficients that have dominated current speech recognition technology. The best error rate reduction of 9% is obtained using the new model, tested on a TIMIT phone classification task, relative to conventional HMM.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".