  In this work we compare different batch construction methods for mini-batch
training of recurrent neural networks. While popular implementations like
TensorFlow and MXNet suggest a bucketing approach to improve the
parallelization capabilities of the recurrent training process, we propose a
simple ordering strategy that arranges the training sequences in a stochastic
alternatingly sorted way. We compare our method to sequence bucketing as well
as various other batch construction strategies on the CHiME-4 noisy speech
recognition corpus. The experiments show that our alternated sorting approach
is able to compete both in training time and recognition performance while
being conceptually simpler to implement.
