  In this work, three lattice-free (LF) discriminative training criteria for
purely sequence-trained neural network acoustic models are compared on LVCSR
tasks, namely maximum mutual information (MMI), boosted maximum mutual
information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate
that, analogous to LF-MMI, a neural network acoustic model can also be trained
from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of
cross-entropy pre-training. Furthermore, experimental results on
Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models
trained with LF-bMMI consistently outperform those trained with plain LF-MMI
and achieve a relative word error rate (WER) reduction of 5% over competitive
temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
