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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".