Recognition of digit strings in noisy speech with limited resources
Bibliographic record
Abstract
Automatic recognition of sequences of spoken digits (e.g., telephone or credit card numbers) can be accomplished with excellent accuracy, even in speaker-independent applications over telephone links. However, even such relatively simple recognition tasks suffer decreased performance in adverse conditions, such as significant background noise or fading on portable telephone channels. If one further imposes significant limitations on the computing resources to be dedicated to a recognition task, then robust, limited-resource speech recognition remains a suitable challenge, even for a vocabulary as simple as the digits. Since connected-digit recognition over telephone lines is a very practical application, the amount of computer resources needed for a given level of recognition accuracy was investigated for different levels and types of acoustic noise. Rather than use a traditional hidden Markov model approach with cepstral analysis, which is computationally intensive and does not always work well under adverse acoustic conditions, simpler spectral analysis was used, combined with a segmental approach. The limited nature of the vocabulary (i.e., ten digits) allows this simpler approach. High recognition accuracy is maintained despite a massive decrease (versus traditional methods) in both memory and computation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".