SDI: A tool for speech differentiation in user identification

Abstract

User identification (UI) with human speech is important in voice biometrics for user authentication. However, the recognized speech is at risk of being spoofed when an intruder attempts to access a voice-secured system. This can be done using a voice spoofing attack, which involves the use of manipulated speech signals for illegal purposes, including accessing authorized users’ data or sensitive information from a secured system. In recent years, different countermeasures have been developed to protect biometric systems from such spoofing attacks, but loopholes remain in such secured systems. Therefore, we urgently need a speech differentiation system that differentiates spoofed speech from bona fide speech in the process of UI. To this end, this paper proposes an anti-spoofing method for user authentication in voice biometrics. A refined mechanism for support vector machine (SVM) was designed; it implements threshold checking and frequency matching mechanisms, resulting in a speech differentiator with integrated SVM (SDI-SVM), or an integrated speech differentiator (SDI) for short. The mechanism was experimentally evaluated, and the results show that SVM outperformed its counterparts in the task of UI. SVM was upgraded into SDI-SVM, which can realize user authentication in voice biometrics by merging speech differentiation into UI. The experimental results also show that SDI-SVM produced better results than those demonstrated in recent works. Thus, the proposed solution can enhance the security of biometric technologies.