Speech recognition in non-stationary adverse environments
Bibliographic record
Abstract
We introduce a new approach, called non-stationary adaptation (NA), to recognize speech under non-stationary adverse environments. Two models are used: one is a speaker-independent hidden Markov model (HMM) for clean speech, the other is an ergodic Markov chain representing the non-stationary adverse environment. Each state in the Markov chain represents one stationary adverse condition and has associated with it an affine transform that is estimated by maximum likelihood linear regression (MLLR). Three kinds of adverse environments are considered: (i) multi-speaker speech recognition where the speaker identity changes randomly and this constitutes a non-stationary adverse condition, (ii) the recognition of speech corrupted by machinegun noise, and (iii) the crosstalk problem. The algorithm is tested on the Nov92 development database of WSJF0 with a vocabulary size of 20000. In multi-speaker speech recognition, NA decreases the error rate by 13.6%. For speech corrupted by machinegun noise, a one-state Markov chain decreases the error rate by 18%, and a two-state Markov chain gives another 14% decrease in error rate. In the crosstalk problem, a one-state Markov chain decreases the error rate by 16.8%. Two-state and three-state Markov chains decrease the error rate by 22% and 24.4%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".