Optimisation of multiple feature stream weights for distributed speech processing in mobile environments
Bibliographic record
Abstract
Mobile environments are highly influenced by ambient noise that can cause a significant deterioration in speech recognition performance. In this study, a new framework integrating a noise‐robust frontend (FE) in distributed speech recognition (DSR) is presented. Using the Aurora‐2 speech database, the authors evaluate the impact of the proposed multidimensional acoustical analysis on the performance of the Mel‐frequency‐based European Telecommunications Standards Institute‐advanced FE (AFE) combined with the Mel‐line spectral frequencies (MLSFs) robust features for highly noisy speech. The stream weights of the resulting multi‐stream hidden Markov models are optimised automatically by deploying a novel approach based on a discriminative model combination. Finally, these features are effectively transformed and reduced using the Karhunen–Loève transform. The proposed MLSF‐based FE (MLSF‐FE) is shown to exhibit a reduction in the relative error rate. Moreover, the proposed FE provides comparable recognition performance to the current DSR‐AFE available in global system of mobile communications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".