Uncertainty for Anti-Spoofing
Bayesian neural networks that say how confident they are when deciding whether a speaker-verification attempt is spoofed.
Bayesianuncertainty estimation
ASVspoofing countermeasure
Second authormy contribution
A spoofing countermeasure that outputs only a hard accept/reject hides something important: how sure it actually is. That matters most exactly where these systems are weakest, on attack types they were never trained against.
This work applies a Bayesian treatment to spoofing detection for automatic speaker verification, so the model produces a calibrated sense of its own uncertainty alongside the decision. That estimate can then gate the outcome, flagging low-confidence cases instead of silently guessing.
Work led by Cagil Suslu at Ozyegin University; I contributed as second author.
Citation
@article{suslu2022uncertainty,
title = {Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach},
author = {Suslu, Cagil and Eren, Eray and Demiroglu, Cenk},
journal = {Speech Communication},
volume = {137},
pages = {44--51},
year = {2022},
doi = {10.1016/j.specom.2021.12.003}
}