  The recurrent neural network transducer (RNN-T) objective plays a major role
in building today's best automatic speech recognition (ASR) systems for
production. Similarly to the connectionist temporal classification (CTC)
objective, the RNN-T loss uses specific rules that define how a set of
alignments is generated to form a lattice for the full-sum training. However,
it is yet largely unknown if these rules are optimal and do lead to the best
possible ASR results. In this work, we present a new transducer objective
function that generalizes the RNN-T loss to accept a graph representation of
the labels, thus providing a flexible and efficient framework to manipulate
training lattices, e.g., for studying different transition rules, implementing
different transducer losses, or restricting alignments. We demonstrate that
transducer-based ASR with CTC-like lattice achieves better results compared to
standard RNN-T, while also ensuring a strictly monotonic alignment, which will
allow better optimization of the decoding procedure. For example, the proposed
CTC-like transducer achieves an improvement of 4.8% on the test-other condition
of LibriSpeech relative to an equivalent RNN-T based system.
