Spoken Pass-Phrase Verification in the i-vector Space
Hossein Zeinali, Lukáš Burget, Hossein Sameti, Honza Černocký · 2018
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.