  In this paper, we present a time-contrastive learning (TCL) based bottleneck
(BN)feature extraction method for speech signals with an application to
text-dependent (TD) speaker verification (SV). It is well-known that speech
signals exhibit quasi-stationary behavior in and only in a short interval, and
the TCL method aims to exploit this temporal structure. More specifically, it
trains deep neural networks (DNNs) to discriminate temporal events obtained by
uniformly segmenting speech signals, in contrast to existing DNN based BN
feature extraction methods that train DNNs using labeled data to discriminate
speakers or pass-phrases or phones or a combination of them. In the context of
speaker verification, speech data of fixed pass-phrases are used for TCL-BN
training, while the pass-phrases used for TCL-BN training are excluded from
being used for SV, so that the learned features can be considered generic. The
method is evaluated on the RedDots Challenge 2016 database. Experimental
results show that TCL-BN is superior to the existing speaker and pass-phrase
discriminant BN features and the Mel-frequency cepstral coefficient feature for
text-dependent speaker verification.
