Publication Details
Spoken Pass-Phrase Verification in the i-vector Space
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Sameti Hossein (FIT)
Černocký Jan, prof. Dr. Ing. (DCGM)
spoken pass-phrase verification, i-vector, speaker verification
The task of spoken pass-phrase verification is to decide whether a test utterance contains the same phrase as given enrollment utterances. Beside other applications, pass-phrase verification can complement an independent speaker verification subsystem in text-dependent speaker verification. It can also be used for liveness detection by verifying that the user is able to correctly respond to a randomly prompted phrase. In this paper, we build on our previous work on i-vector based text-dependent speaker verification, where we have shown that i-vectors extracted using phrase specific Hidden Markov Models (HMMs) or using Deep Neural Network (DNN) based bottle-neck (BN) features help to reject utterances with wrong pass-phrases. We apply the same i-vector extraction techniques to the stand-alone task of speakerindependent spoken pass-phrase classification and verification. The experiments on RSR2015 and RedDots databases show that very simple scoring techniques (e.g. cosine distance scoring) applied to such i-vectors can provide results superior to those previously published on the same data.
@inproceedings{BUT155079,
author="Hossein {Zeinali} and Lukáš {Burget} and Hossein {Sameti} and Jan {Černocký}",
title="Spoken Pass-Phrase Verification in the i-vector Space",
booktitle="Proceedings of Odyssey 2018",
year="2018",
journal="Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland",
volume="2018",
number="6",
pages="372--377",
publisher="International Speech Communication Association",
address="Les Sables d´Olonne",
doi="10.21437/Odyssey.2018-52",
issn="2312-2846",
url="https://www.fit.vut.cz/research/publication/11791/"
}