  Speaker embeddings are continuous-value vector representations that allow
easy comparison between voices of speakers with simple geometric operations.
Among others, i-vector and x-vector have emerged as the mainstream methods for
speaker embedding. In this paper, we illustrate the use of modern computation
platform to harness the benefit of GPU acceleration for i-vector extraction. In
particular, we achieve an acceleration of 3000 times in frame posterior
computation compared to real time and 25 times in training the i-vector
extractor compared to the CPU baseline from Kaldi toolkit. This significant
speed-up allows the exploration of ideas that were hitherto impossible. In
particular, we show that it is beneficial to update the universal background
model (UBM) and re-compute frame alignments while training the i-vector
extractor. Additionally, we are able to study different variations of i-vector
extractors more rigorously than before. In this process, we reveal some
undocumented details of Kaldi's i-vector extractor and show that it outperforms
the standard formulation by a margin of 1 to 2% when tested with VoxCeleb
speaker verification protocol. All of our findings are asserted by ensemble
averaging the results from multiple runs with random start.
