New low rate wavelet models for the recognition of single spoken digits
Bibliographic record
Abstract
This paper describes three models acquired by applying various wavelet analysis techniques to subwords for the purpose of speaker independent single digit recognition. We emphasize the parameterization of the subwords according to a Mel scale in the cases of the sampled continuous wavelet transform (SCWT) and the wavelet packet decomposition (WPD). When using the discrete wavelet transform (DWT), a logarithmic segmentation is obtained and with it comes a very low parameter representation with a reduction of 3:1 when compared with the other two introduced models and with the Mel scale model. The DWT model has advantage over the other two due to its simplicity and fast implementation. Previous work by Phillips, Tosuner and Robertson (1995), based on preprocessing using traditional Fourier transform (FT) followed by a radial basis functions artificial neural network (RBF-ANN) yielded recognition in 90% range. Our results show that these new models outperformed the Mel scale model.
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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) | 0.000 | 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".