On the study of replay and voice conversion attacks to text-dependent speaker verification

Abstract

Automatic speaker verification (ASV) is to automatically accept or reject a claimed identity based on a speech sample. Recently, individual studies have confirmed the vulnerability of state-of-the-art text-independent ASV systems under replay, speech synthesis and voice conversion attacks on various databases. However, the behaviours of text-dependent ASV systems have not been systematically assessed in the face of various spoofing attacks. In this work, we first conduct a systematic analysis of text-dependent ASV systems to replay and voice conversion attacks using the same protocol and database, in particular the RSR2015 database which represents mobile device quality speech. We then analyse the interplay of voice conversion and speaker verification by linking the voice conversion objective evaluation measures with the speaker verification error rates to take a look at the vulnerabilities from the perspective of voice conversion.

Anti-Spoofing for Text-Independent Speaker Verification: An Initial Database, Comparison of Countermeasures, and Human Performance

“…Here nx(n) is the re-scaled signal of x(n). 2) Compute the cepstrally-smoothed power spectrum 14 |S(ω)| 2 of |X(ω)| 2 . 3) Compute the MGD spectrum (R and I denote the real and imaginary parts of the spectrum)…”

“…A major concern, however, when deploying an ASV system, is its resilience to a spoofing attack. As identified in [8], there are at least four types of spoofing attack: impersonation [9], [10], [11], replay [12], [13], [14], speech synthesis [15], [16] and voice conversion [17], [18], [19], [20], [21]. Among the four types of spoofing attack, replay, speech synthesis, and voice conversion present the highest risk to ASV systems [8].…”

“…Among the four types of spoofing attack, replay, speech synthesis, and voice conversion present the highest risk to ASV systems [8]. Although replay might be the most common spoofing technique which presents a risk to both text-dependent and textindependent ASV systems [12], [13], [14], it is not viable for the generation of utterances of specific content, such as would be required to maintain a live conversation in a callcenter application. On the other hand, open-source software for state-of-the-art speech synthesis and voice conversion is readily available (e.g., Festival 3 and Festvox 4 ), making these two approaches perhaps the most accessible and effective means to carry out spoofing attacks, and therefore presenting a serious risk to deployed ASV systems [8].…”

Guest Editorial: Immersive Audio/Visual Systems

“…Wu et al [13] conduct a systematic analysis of text-dependent speaker verification systems to speech replay and voice conversion attacks. Specifically, using the same protocol and database, they analyze the interplay of voice conversion and speaker verification by linking the voice conversion objective evaluation measures with the speaker verification error rates to investigate the vulnerabilities from the perspective of voice conversion.…”

Speaker verification anti-spoofing using linear prediction residual phase features

“…For the ASV systems, impersonation [8], replay [9], speech synthesis (SS) [6] and voice conversion (VC) [10] are the four major direct spoofing attack types against ASV systems [11]. Among these four attack types, impersonation is less likely since it requires a professional skill to mimic a target speaker's voice.…”