Optimal feature vector for speech recognition of unequally segmented spoken digits
Bibliographic record
Abstract
We describe a model obtained by applying the discrete wavelet transform (DWT) to unequally segmented digits. Each signal is divided with a pre-determined segmentation into a maximum of five subwords. The purpose is speaker independent single digit recognition. The parameterization of the subwords is accomplished by measuring its energy contents after decomposing it with the DWT. This model uses one coefficient per subword and produces up to a 99% recognition rate. It is superior in its class due to the high reduction in the size of the feature vector and consequently in the speed of processing. Typically the reduction is 20:1 if compared with the traditional Mel-scale model. A successful attempt to classify vowels and accurately identify digits visibly using the proposed model is undertaken. A radial basis function artificial neural network (RBF-ANN) is employed for the recognition tasks and for the comparison of the proposed model with the Fourier one. We use orthogonal wavelets from the Daubechies (1992) set. Also the performances of some biorthogonal wavelets are included.
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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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".