Efficient classification of noisy speech using neural networks
Bibliographic record
Abstract
The classification of active speech vs. inactive speech in noisy speech is an important part of speech applications, typically in order to achieve a lower bit-rate. In this work, the error rates for raw classification (i.e. with no hangover mechanism) of noisy speech obtained with traditional classification algorithms are compared to the rates obtained with neural network classifiers, trained with different learning algorithms. The traditional classification algorithms used are the linear classifier, some nearest neighbor classifiers and the quadratic Gaussian classifier. The training algorithms used for the neural networks classifiers are the extended Kalman filter and the Levenberg-Marquadt algorithm. An evaluation of the computational complexity for the different classification algorithms is presented. Our noisy speech classification experiments show that using neural network classifiers typically produces a more accurate and more robust classification than other traditional algorithms, while having a significantly lower computational complexity. Neural network classifiers may therefore be a good choice for the core component of a noisy speech classifier, which would typically also include a hangover mechanism and possibly a speech enhancement algorithm.
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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".