  In this letter, we propose a vocal tract length (VTL) perturbation method for
text-dependent speaker verification (TD-SV), in which a set of TD-SV systems
are trained, one for each VTL factor, and score-level fusion is applied to make
a final decision. Next, we explore the bottleneck (BN) feature extracted by
training deep neural networks with a self-supervised objective, autoregressive
predictive coding (APC), for TD-SV and compare it with the well-studied
speaker-discriminant BN feature. The proposed VTL method is then applied to APC
and speaker-discriminant BN features. In the end, we combine the VTL
perturbation systems trained on MFCC and the two BN features in the score
domain. Experiments are performed on the RedDots challenge 2016 database of
TD-SV using short utterances with Gaussian mixture model-universal background
model and i-vector techniques. Results show the proposed methods significantly
outperform the baselines.
