Speech Based Human Authentication on Smartphones

Voice has been used as biometrics for human authentication because different people have different voice characteristics due to different vocal tract shapes and intonations. However, traditional voice based human authentication is subject to four types of attacks: impersonation, voice conversion, synthesis and voice replay. In this paper, we propose SpeakPrint, an ultrasound based human speech authentication scheme for smartphones which is resistant for these attacks. Compared with traditional speech authentication system which focuses on what a user speaks, SpeakPrint captures how a user speaks by recording mouth and vocal movement through ultrasound signal at the same time. Our key insight is that for the valid user, features extracted from voice signal should be consistent with his mouth and vocal movement recorded from ultrasound signal, while an imitator or an audio player can't produce the same signals in ultrasound domain. SpeakPrint extracts MFCC feature in normal voice frequency and MMSI features from ultrasound signal. An SVM classifier is trained to detect these attacks by comparing above feature differences. We implemented SpeakPrint on Samsung S5 and conducted experiments on 40 users. Experimental results show that SpeakPrint can detect replay attacks with 100% accuracy and replay attack with lip synching for 99.12% for passphrases longer than five words. This technology can be used in multi-factor authentication systems, where multiple authentication mechanisms are used to achieve defense in depth.