  In this paper, we propose a novel method that trains pass-phrase specific
deep neural network (PP-DNN) based auto-encoders for creating augmented data
for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is
trained using the utterances of a particular pass-phrase available in the
target enrollment set with two methods: (i) transfer learning and (ii) training
from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs
and the output from each PP-DNN at frame-level is considered one new set of
generated data. The generated data from each PP-DNN is then used for building a
TD-SV system in contrast to the conventional method that considers only the
evaluation data available. The proposed approach can be considered as the
transformation of data to the pass-phrase specific space using a non-linear
transformation learned by each PP-DNN. The method develops several TD-SV
systems with the number equal to the number of PP-DNNs separately trained for
each pass-phrases for the evaluation. Finally, the scores of the different
TD-SV systems are fused for decision making. Experiments are conducted on the
RedDots challenge 2016 database for TD-SV using short utterances. Results show
that the proposed method improves the performance for both conventional
cepstral feature and deep bottleneck feature using both Gaussian mixture model
- universal background model (GMM-UBM) and i-vector framework.
