Improving automatic speech recognition via better analysis and adaptation
Bibliographic record
Abstract
One way to improve automatic speech recognition (ASR) systems is to reduce the mismatch between system training and operating conditions, as such mismatch seriously degrades performance. We have developed model adaptation techniques able to adapt to various speech environments without modifying ASR systems, and have developed an appropriate feature transformation scheme for the Mel-frequency cepstral coefficients (MFCC), a popular front-end feature of ASR systems. We use maximum a posteriori model adaptation and a method based on Bayesian parametric representation. Feature transformation aims to maximize the desired source of information for a given speech signal in the front-end features and to minimize undesired sources. Frequency-domain autoregressive modeling and a segmentation algorithm are being developed, e.g., to segment a speech signal into syllablelike units. We also introduce a new speech-processing front-end feature that performs better than the existing MFCC, as well as a log-energy dynamic range normalization technique for ASR in adverse conditions. In addition, we have developed a continuous ASR method that exploits the advantages of syllable and phoneme-based subword unit models. [Work supported by NSERC-Canada and Prompt-Quebec.]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".