  Recently, RNN-Transducers have achieved remarkable results on various
automatic speech recognition tasks. However, lattice-free sequence
discriminative training methods, which obtain superior performance in hybrid
models, are rarely investigated in RNN-Transducers. In this work, we propose
three lattice-free training objectives, namely lattice-free maximum mutual
information, lattice-free segment-level minimum Bayes risk, and lattice-free
minimum Bayes risk, which are used for the final posterior output of the
phoneme-based neural transducer with a limited context dependency. Compared to
criteria using N-best lists, lattice-free methods eliminate the decoding step
for hypotheses generation during training, which leads to more efficient
training. Experimental results show that lattice-free methods gain up to 6.5%
relative improvement in word error rate compared to a sequence-level
cross-entropy trained model. Compared to the N-best-list based minimum Bayes
risk objectives, lattice-free methods gain 40% - 70% relative training time
speedup with a small degradation in performance.
