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Applying x-vectors for speaker verification has recently attracted greatinterest, with the focus being on text-independent speaker verification. Inthis paper, we study x-vectors for text-dependent speaker verification (TD-SV),which remains unexplored. We further investigate the impact of the differentbottleneck (BN) features on the performance of x-vectors, including therecently-introduced time-contrastive-learning (TCL) BN features andphone-discriminant BN features. TCL is a weakly supervised learning approachthat constructs training data by uniformly partitioning each utterance into apredefined number of segments and then assigning each segment a class labeldepending on their position in the utterance. We also compare TD-SV performancefor different modeling techniques, including the Gaussian mixturemodels-universal background model (GMM-UBM), i-vector, and x-vector.Experiments are conducted on the RedDots 2016 challenge database. It is foundthat the type of features has a marginal impact on the performance of x-vectorswith the TCL BN feature achieving the lowest equal error rate, while the impactof features is significant for i-vector and GMM-UBM. The fusion of x-vector andi-vector systems gives a large gain in performance. The GMM-UBM technique showsits advantage for TD-SV using short utterances.

On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors