  Recently, speaker embeddings extracted with deep neural networks became the
state-of-the-art method for speaker verification. In this paper we aim to
facilitate its implementation on a more generic toolkit than Kaldi, which we
anticipate to enable further improvements on the method. We examine several
tricks in training, such as the effects of normalizing input features and
pooled statistics, different methods for preventing overfitting as well as
alternative non-linearities that can be used instead of Rectifier Linear Units.
In addition, we investigate the difference in performance between TDNN and CNN,
and between two types of attention mechanism. Experimental results on Speaker
in the Wild, SRE 2016 and SRE 2018 datasets demonstrate the effectiveness of
the proposed implementation.
