Scholarlink

<p>Spoofing-robust automatic speaker verification (SASV) 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 (PMF) of waveform amplitudes in the time-domain. Using the logical access (LA) database from the ASVspoof2019 challenge 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, and 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 minimum tandem detection cost function (t-DCF), the system’s performance is 0.269 for the gender-dependent CM system and 0.317 for the gender-independent system.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spoofing-robust speaker verification based on time-domain embedding

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月18日

查看全文
  • Avishai Weizman,
  • Yehuda Ben-Shimol,
  • Itshak Lapidot

摘要

Spoofing-robust automatic speaker verification (SASV) 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 (PMF) of waveform amplitudes in the time-domain. Using the logical access (LA) database from the ASVspoof2019 challenge 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, and 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 minimum tandem detection cost function (t-DCF), the system’s performance is 0.269 for the gender-dependent CM system and 0.317 for the gender-independent system.