Speaker identification by computer and human evaluated on the SPIDRE corpus
Bibliographic record
Abstract
INTRODUCTION Although many experiments on clean speech report high identification rates for computer systems, results on noisy telephone speech with different handsets are usually too poor for practical identification tasks (noise, limited bandwidth, effect of the channel, telephone handsets variability)[1]. What would be the identification rate of humans in the same conditions? A reference is necessary in order to evaluate the performance of computer systems. The comparison between computer and human has been already made. For a review one can refer for example to the work by Doddingtion [2]. As the performance of human has been chown to be dependent of the speech nature, we propose to examine the effect of telephone handset variability for text-independent speaker identification of telephone speech. We report human and computer speaker identification with the SPIDRE database. Section 2 describes the experimental conditions while section 3 and 4 are the results and discussion. Sect
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".