  In this paper, we present the DKU system for the speaker recognition task of
the VOiCES from a distance challenge 2019. We investigate the whole system
pipeline for the far-field speaker verification, including data pre-processing,
short-term spectral feature representation, utterance-level speaker modeling,
back-end scoring, and score normalization. Our best single system employs a
residual neural network trained with angular softmax loss. Also, the weighted
prediction error algorithms can further improve performance. It achieves 0.3668
minDCF and 5.58% EER on the evaluation set by using a simple cosine similarity
scoring. Finally, the submitted primary system obtains 0.3532 minDCF and 4.96%
EER on the evaluation set.
