  This paper addresses the robust speech recognition problem as an adaptation
task. Specifically, we investigate the cumulative application of adaptation
methods. A bidirectional Long Short-Term Memory (BLSTM) based neural network,
capable of learning temporal relationships and translation invariant
representations, is used for robust acoustic modelling. Further, i-vectors were
used as an input to the neural network to perform instantaneous speaker and
environment adaptation, providing 8\% relative improvement in word error rate
on the NIST Hub5 2000 evaluation test set. By enhancing the first-pass i-vector
based adaptation with a second-pass adaptation using speaker and environment
dependent transformations within the network, a further relative improvement of
5\% in word error rate was achieved. We have reevaluated the features used to
estimate i-vectors and their normalization to achieve the best performance in a
modern large scale automatic speech recognition system.
