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Record W2109740207 · doi:10.1109/isccsp.2008.4537397

A playback attack detector for speaker verification systems

2008· article· en· W2109740207 on OpenAlex
Wei Shang, M. Stevenson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceDetectorSimilarity (geometry)Frame (networking)Set (abstract data type)Speech recognitionCosine similarityFeature (linguistics)Speaker verificationUtteranceFast Fourier transformArtificial intelligencePattern recognition (psychology)Speaker recognitionAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

A playback attack detector (PAD), which can be mobilized in guarding speaker verification systems against playback attacks, is described in this paper. To detect playback attacks, the PAD uses a feature set called peakmap, which includes the frame and FFT bin numbers of the five highest spectral peaks from each of the voiced frames in an utterance. During the detection, the peakmap of the incoming recording is first extracted and then compared to those of all the other recordings that are stored at the system end. Each comparison will yield a similarity score that represents the level of similarity between the two recordings. The incoming recording is declared to be a playback recording if its maximum similarity score is above a threshold.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.097
GPT teacher head0.270
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations23
Published2008
Admission routes2
Has abstractyes

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