Accuracy of speaker verification via orthogonal parameters for noisy speech
Bibliographic record
Abstract
An important problem in speaker verification systems arises, when the speech inputs are noise corrupted with signal to noise ratios in the range of 10 to 30 dB (noise assumed to be zero mean and white). This paper deals with the accuracy of speaker verification algorithms derived from an orthogonal parameter representation of speech. Initially, the investigations are directed to evaluate the sensitivity of orthogonal parameters to the level of noise in the speech signal. The accuracy of verification is then determined, using only those parameters that are least sensitive to additive noise. The influence of the order of the linear prediction model on verification is also studied. The verification algorithm is based on the distance measure used by Sambur. Finally thresholds are established to determine the proper choice of orthogonal parameters (used in distance computation) and the order of the linear prediction model for a given signal to noise ratio in the speech signal. The above study is then used to evaluate the accuracy of verification when speech inputs are obtained from a noisy telephone channel.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, 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".