Efficient automatic recognition of spoken digit strings
Bibliographic record
Abstract
Automatic recognition of spoken digit sequences (such as credit card numbers) is now feasible even in speaker-independent applications over the telephone. However, all recognition tasks have lower performance in noisy conditions. If significant limitations are also imposed on the computational resources used for recognition, then robust speech recognition is still a significant challenge, even for a simple digit vocabulary. Since recognition of continuously spoken digits over telephone links is a very practical application, such recognition was investigated here under different conditions. Traditional hidden Markov model approaches with cepstral analysis were not used, because they are computationally intensive and have not always worked well under adverse acoustic conditions. Simpler spectral analysis was used, combined with a segmental approach. The analysis focuses on locations of spectral peaks, similar to formant tracking, but without the need to estimate peaks for all time frames. The limited nature of the vocabulary (i.e., ten digits) allows this simpler approach. High recognition accuracy is maintained despite being very efficient in both memory and computation. Recent progress will be reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".