  Text-dependent speaker verification is becoming popular in the speaker
recognition society. However, the conventional i-vector framework which has
been successful for speaker identification and other similar tasks works
relatively poorly in this task. Researchers have proposed several new methods
to improve performance, but it is still unclear that which model is the best
choice, especially when the pass-phrases are prompted during enrollment and
test. In this paper, we introduce four modeling methods and compare their
performance on the newly published RedDots dataset. To further explore the
influence of different frame alignments, Viterbi and forward-backward
algorithms are both used in the HMM-based models. Several bottleneck features
are also investigated. Our experiments show that, by explicitly modeling the
lexical content, the HMM-based modeling achieves good results in the
fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and
i-vector/HMM are not as successful. In both conditions, the forward-backward
algorithm brings more benefits to the i-vector/HMM system. Additionally, we
also find that even though bottleneck features perform well for
text-independent speaker verification, they do not outperform MFCCs on the most
challenging Imposter-Correct trials on RedDots.
