Spoken Pass-Phrase Verification in the i-vector Space

Hossein Zeinali, Lukas Burget, Hossein Sameti, Honza Cernocky


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 speaker-independent 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.


 DOI: 10.21437/Odyssey.2018-52

Cite as: Zeinali, H., Burget, L., Sameti, H., Cernocky, H. (2018) Spoken Pass-Phrase Verification in the i-vector Space . Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, 372-377, DOI: 10.21437/Odyssey.2018-52.


@inproceedings{Zeinali2018,
  author={Hossein Zeinali and Lukas Burget and Hossein Sameti and Honza Cernocky},
  title={Spoken Pass-Phrase Verification in the i-vector Space	},
  year=2018,
  booktitle={Proc. Odyssey 2018 The Speaker and Language Recognition Workshop},
  pages={372--377},
  doi={10.21437/Odyssey.2018-52},
  url={http://dx.doi.org/10.21437/Odyssey.2018-52}
}