Nonlinear noise compensation in feature domain for speech recognition with numerical methods
Bibliographic record
Abstract
In this paper, we propose to compensate noise in the log-spectral domain for robust speech recognition based on a nonlinear environmental model. In our approach, starting from the original nonlinear speech distortion model in the feature domain, we derive the minimum mean square error (MMSE) estimation of clean speech signal given a noisy observation, which turns out to be an integral of a complex nonlinear function. In this work, we propose to use a numerical method to solve the above nonlinear integral. It requires higher computational complexity than the normal linear approximation methods but it is usually affordable since calculation is performed entirely in the pre-processing feature domain without involving any change in speech decoders. Experimental results show that the proposed nonlinear method outperforms the conventional vector Taylor series (VTS) method in terms of ASR performance when dealing with artificial white Gaussian noise as well as true hands-free noisy speech, especially in low SNR levels.
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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".