  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.
