Publication Details
Spoken Pass-Phrase Verification in the i-vector Space
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Sameti Hossein (SHARIF)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
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{FITPUB11791, author = "Hossein Zeinali and Luk\'{a}\v{s} Burget and Hossein Sameti and Jan \v{C}ernock\'{y}", title = "Spoken Pass-Phrase Verification in the i-vector Space", pages = "372--377", booktitle = "Proceedings of Odyssey 2018", journal = "Proceedings of Odyssey: The Speaker and Language Recognition Workshop", volume = 2018, number = 6, year = 2018, location = "Les Sables dOlonne, FR", publisher = "International Speech Communication Association", ISSN = "2312-2846", doi = "10.21437/Odyssey.2018-52", language = "english", url = "https://www.fit.vut.cz/research/publication/11791" }