  Speaker recognition performance has been greatly improved with the emergence
of deep learning. Deep neural networks show the capacity to effectively deal
with impacts of noise and reverberation, making them attractive to far-field
speaker recognition systems. The x-vector framework is a popular choice for
generating speaker embeddings in recent literature due to its robust training
mechanism and excellent performance in various test sets. In this paper, we
start with early work on including invariant representation learning (IRL) to
the loss function and modify the approach with centroid alignment (CA) and
length variability cost (LVC) techniques to further improve robustness in
noisy, far-field applications. This work mainly focuses on improvements for
short-duration test utterances (1-8s). We also present improved results on
long-duration tasks. In addition, this work discusses a novel self-attention
mechanism. On the VOiCES far-field corpus, the combination of the proposed
techniques achieves relative improvements of 7.0% for extremely short and 8.2%
for full-duration test utterances on equal error rate (EER) over our baseline
system.
