  This paper presents the Speech Technology Center (STC) speaker recognition
(SR) systems submitted to the VOiCES From a Distance challenge 2019. The
challenge's SR task is focused on the problem of speaker recognition in single
channel distant/far-field audio under noisy conditions. In this work we
investigate different deep neural networks architectures for speaker embedding
extraction to solve the task. We show that deep networks with residual frame
level connections outperform more shallow architectures. Simple energy based
speech activity detector (SAD) and automatic speech recognition (ASR) based SAD
are investigated in this work. We also address the problem of data preparation
for robust embedding extractors training. The reverberation for the data
augmentation was performed using automatic room impulse response generator. In
our systems we used discriminatively trained cosine similarity metric learning
model as embedding backend. Scores normalization procedure was applied for each
individual subsystem we used. Our final submitted systems were based on the
fusion of different subsystems. The results obtained on the VOiCES development
and evaluation sets demonstrate effectiveness and robustness of the proposed
systems when dealing with distant/far-field audio under noisy conditions.
