Spoofing-robust speaker verification based on time-domain embedding

Spoofing-robust automatic speaker verification technology is used to safeguard voice-based authentication systems from fraudulent attempts. Such a system should be able to verify that the speech was spoken by the target speaker, as in standard automatic speaker verification, and should also be robust against spoofing attacks. This research employs an understandable and explicable embedding derived from the probability mass function of waveform amplitudes in the time domain. Using the logical access (LA) from the ASVspoof2019 database for evaluation, we show that the performance of the countermeasure (CM) system is enhanced when it is gender dependent. The CM system demonstrated an equal error rate (EER) of 8.6% on the evaluation set for the male gender, with an EER of 10.1% for the female gender. In contrast, a gender-independent CM system exhibited an EER of 10.2%. As quantified by the detection cost function for tandem assessment (t-DCF), the system's performance is 0.269 for the gender-dependent CM system and 0.317 for the gender-independent system.