  Mismatched data is a challenging problem for automatic speech recognition
(ASR) systems. One of the most common techniques used to address mismatched
data is multi-style training (MTR), a form of data augmentation that attempts
to transform the training data to be more representative of the testing data;
and to learn robust representations applicable to different conditions. This
task can be very challenging if the test conditions are unknown. We explore the
impact of different MTR styles on system performance when testing conditions
are different from training conditions in the context of deep neural network
hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created
using the LibriSpeech corpus, where we isolate the effect of different MTR
styles on final system performance. We evaluate our findings on a South African
call centre dataset that contains noisy, WAV49-encoded audio.
