Application of vector quantized hidden Markov modeling to telephone network based connected digit recognition
Bibliographic record
Abstract
Connected digit speech recognition in the telephone network is becoming increasingly more important as the demand for speech technology becomes widespread. In the past few years, several highly successful techniques for recognizing spoken connected digit strings have been proposed. Although these techniques have been applied to non-telephone based speech [e.g. Texas Instruments database], they have produced high recognition performance. Further, similar levels of performances have been demonstrated using discrete density and continuous density based hidden Markov models (HMMs). The success of the vector quantized (VQ) modeling approach, in particular, is encouraging and rather important from the viewpoint of computational efficiency. This paper presents a study of connected digit recognition on telephone network based data using VQ HMMs. We investigate several factors affecting the error rate of VQ HMMs-such as maximum mutual information (MMI) training, sender modeling, and codebook size-and measure their contributions to recognition accuracy. The model architecture, number of states and transitions, is also optimized and its contribution to overall performance discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".