Hello, everyone. My name is Li Lu, I’m now a Research Professor of Zhejiang University.
This work was done when I was a Ph.D. student at Shanghai Jiao Tong University. It’s my
pleasure to present our work, VocalLock: Sensing Vocal Tract for Passphrase-Independent User
Authentication Leveraging Acoustic Signals on Smartphones. Now let me begin my talk.
Mobile devices are now popular in our daily life and work. Thanks to its convenient mobility
and powerful capability, we use the devices to store private information,
receive personalized recommendation, and even conduct critical operations. So, the user
authentication become vital to protect the user privacy.
Representative solutions include fingerprint, face, voiceprint, and so on.
Though these widely-employed approaches contributes to improving the authentication experience,
they may fail to distinguish a real user from a spoofing replayed video or recording. This
inspires the development of liveness verification techniques to enhance the security of these
approaches. Instead, recent studies turn to investigate
the feasibility of employing human behavior for authentication, which naturally embeds
the liveness verification. Along this direction, we consider the gradually
wide deployment of voice assistant, aiming to authenticating users by sensing the vocal
tract instead of voiceprint.
Existing speaking behavior-based approaches authenticate users in a passphrase-dependent
manner, requiring the user to remember the passphrase in each login. Similar situations
appear in the text-dependent voiceprint authentication. This requirement significantly degrades the
user experience. Revisiting voiceprint authentication, we find
there are two kinds of authentication manner, they are text-dependent and text-independent.
Compared with text-dependent authentication, text-independent one provides more
flexible authentication manner. So it can help to improve the user experience and support
a wider range of applications, such as phone fraud detection.
Along this direction, our goal is to implement a passphrase-independent authentication by
sensing the vocal tract behaviors.
So how to implement such an authentication? We seek for the help from the text-independent
voiceprint authentication. These solutions usually employ statistical techniques, like
Gaussian Mixture Model. So we further use the principle component analysis on our behavioral
features and obtain the following preliminary results.
We can find that from the perspective of passphrase, the principle components could be distinguished.
And from the perspective of users, the principle components could still be roughly distinct,
which demonstrates the feasibility of the technical way.
Following the basic idea, we first employ FMCW on acoustic signals to sense the vocal tract,
including the static shape and dynamic behaviors of the vocal tract. We first set
the two key parameters, they are the period and bandwidth, as 20ms and 16-24kHz, considering
the vocal tract sensing requirement and other practical issues, including the human auditory and
device limitation. Then, we segment each speaking word with the
audible voice detection, and perform the dechirp operation to obtain the frequency difference.
In the received signals, except for the targeted signal, there are still LOS and other reflected signals,
which should be eliminated. By developing the STFT-based heuristic algorithm
and verifying the ToF, we could eliminate them.
After that, following the similar solution for text-independent speaker recognition problem,
we employ Gaussian Mixture Model-Uniform Background Model with the Mel-frequency cepstrum coefficient features
to construct the passphrase-independent authentication model.
Since using FMCW, we only have the frequency difference features representing the vocal
tract. Hence, we design an encoder-decoder structure feature transfer network to obtain the relative MFCC features.
Afterwards, we could directly train a GMM—UBM model for the authentication.
Then comes to the evaluation results. In the passphrase-independent manner, our system
could achieve an overall 91% accuracy. Compared with other solutions, we find our solution
could achieve similar performance, demonstrating the effectiveness of our proposed solution.
On the other hand, we find the false reject rate are all below 2%, and for 82.7% samples,
the users could successfully login with only 1 speaking. These results validate the good
user experience of our solution.
In conclusion, this paper investigates the feasibility of realizing the passphrase-independent
authentication, and further design FMCW-based sensing solution, and GMM-UBM-based authentication model.
Result shows that our solution could achieve competitive performance in both authentication
accuracy and user experience.
That’s all, thank you for listening.