Spoofing voice verification systems with statistical speech synthesis using limited adaptation data

Abstract

State-of-the-art speaker verification systems are vulnerable to spoofing attacks using speech synthesis. To solve the issue, high-performance synthetic speech detectors (SSDs) for attack methods have been proposed recently. Here, as opposed to developing new detectors, we investigate new attack strategies. Investigating new techniques that are specifically tailored for spoofing attacks that can spoof the voice verification system and are difficult to detect is expected to increase the security of voice verification systems by enabling the development of better detectors. First, we investigated the vulnerability of an i-vector based verification system to attacks using statistical speech synthesis (SSS), with a particular focus on the case where the attacker has only a very limited amount of data from the target speaker. Even with a single adaptation utterance, the false alarm rate was found to be 23%. Still, SSS-generated speech is easy to detect (Wu et al., 2015a, 2015b), which dramatically reduces its effectiveness. For more effective attacks with limited data, we propose a hybrid statistical/concatenative synthesis approach and show that hybrid synthesis significantly increases the false alarm rate in the verification system compared to the baseline SSS method. Moreover, proposed hybrid synthesis makes detecting synthetic speech more difficult compared to SSS even when very limited amount of original speech recordings are available to the attacker. To further increase the effectiveness of the attacks, we propose a linear regression method that transforms synthetic features into more natural features. Even though the regression approach is more effective at spoofing the detectors, it is not as effective as the hybrid synthesis approach in spoofing the verification system. An interpolation approach is proposed to combine the linear regression and hybrid synthesis methods, which is shown to provide the best spoofing performance in most cases.