Audio recordings dataset of genuine and replayed speech at both ends of a telecommunication channel
Bibliographic record
Abstract
The recordings in this database were collected for the purpose of evaluating the ability of a copy-detection based playback attack detector to safeguard a remote-access speaker-verified and passphrase-protected system from playback attacks. The database includes multiple utterances of the same phrase by the same person in addition to a variety of distorted versions of many of the utterances. Multiple distortions of an utterance were obtained, in part, by simultaneously recording the utterance at both ends of a telecommunication channel – using a digital voice recorder to obtain the user-end (i.e., in-person) recording and a telephony board to obtain the system-end recording. While the former suffers little distortion, the latter suffers the “non-stationary” distortion imposed by the channel. Additional distortions of the same utterance were captured at the system-end of the channel when the in-person recording was replayed at the user-end; these additional recordings simulate playback attacks and suffer the distortion imposed by both the playback device and the channel. The database may be used: to evaluate the vulnerability of a speaker verification system (SVS) to playback attacks; to evaluate the performance of a copy-detection or distortion-detection based playback attack detector (PAD); to evaluate the overall security of a speaker verification system in tandem with a playback attack countermeasure; or to investigate the distortion imposed by various telecommunication channels and/or playback speakers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".