Combination of Recognizers and Fusion of Features Approach to Missing Data ASR Under Non-Stationary Noise Conditions
Bibliographic record
Abstract
The difficulty of ASR under non-stationary noise conditions is a major contributing factor hindering the widespread deployment of ASR systems. Bottom up techniques such as speech noise separation and top down methods to adapt the acoustic model to the environment have been applied to address the issue. The missing data approach to ASR improves upon existing techniques basing recognition solely on the reliable components of the signal and has been demonstrated as an effective method to handle non-stationarity. Proposed in this paper is a novel technique whereby ASR using missing data theory under non-stationary noise conditions is improved by use of a fusion of models at the decision level. This fused model introduces more resilient features to the missing data decode process. The fused decoder is found to significantly increase recognition performance over conventional missing data techniques. A major finding in this paper is when the fused decoder exhibits the fusion of bottom up and top down processes. Under this condition, the proposed combination of recognizers technique is found to outperform all other tested ASR systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".