TY - GEN
T1 - Spoofing-Robust Speaker Verification Based on Time-Domain Embedding
AU - Weizman, Avishai
AU - Ben-Shimol, Yehuda
AU - Lapidot, Itshak
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Spoofing-robust speaker verification technology serves to safeguard voice-based authentication systems from fraudulent attempts. Such a system should be capable of detecting spoofed voice segments and verifying voice segments identified as genuine as originating from the real speaker. This research employs an understandable and explainable embedding based on the probability mass function of waveform amplitudes in the time domain. The results demonstrate that the performance of the countermeasure (CM) system is enhanced when it is gender dependent. The ASVspoof2019 challenge, logical access (LA) database was employed for evaluation purposes. The CM system demonstrated an equal error rate (EER) of 9.2% 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%. The system’s performance, as quantified by the detection cost function for tandem assessment (t-DCF), is 0.262 for the gender-dependent system and 0.328 for the gender-independent system.
AB - Spoofing-robust speaker verification technology serves to safeguard voice-based authentication systems from fraudulent attempts. Such a system should be capable of detecting spoofed voice segments and verifying voice segments identified as genuine as originating from the real speaker. This research employs an understandable and explainable embedding based on the probability mass function of waveform amplitudes in the time domain. The results demonstrate that the performance of the countermeasure (CM) system is enhanced when it is gender dependent. The ASVspoof2019 challenge, logical access (LA) database was employed for evaluation purposes. The CM system demonstrated an equal error rate (EER) of 9.2% 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%. The system’s performance, as quantified by the detection cost function for tandem assessment (t-DCF), is 0.262 for the gender-dependent system and 0.328 for the gender-independent system.
KW - Anti-Spoofing
KW - Automatic Speaker Verification
KW - Countermeasure System
KW - Gender Classification
KW - t-DCF
UR - http://www.scopus.com/inward/record.url?scp=85214193360&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-76934-4_4
DO - https://doi.org/10.1007/978-3-031-76934-4_4
M3 - Conference contribution
SN - 9783031769337
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 78
BT - Cyber Security, Cryptology, and Machine Learning - 8th International Symposium, CSCML 2024, Proceedings
A2 - Dolev, Shlomi
A2 - Elhadad, Michael
A2 - Kutyłowski, Mirosław
A2 - Persiano, Giuseppe
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024
Y2 - 19 December 2024 through 20 December 2024
ER -