  Most existing sequence labelling models rely on a fixed decomposition of a
target sequence into a sequence of basic units. These methods suffer from two
major drawbacks: 1) the set of basic units is fixed, such as the set of words,
characters or phonemes in speech recognition, and 2) the decomposition of
target sequences is fixed. These drawbacks usually result in sub-optimal
performance of modeling sequences. In this pa- per, we extend the popular CTC
loss criterion to alleviate these limitations, and propose a new loss function
called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically
learns the best set of basic units (grams), as well as the most suitable
decomposition of tar- get sequences. Unlike CTC, Gram-CTC allows the model to
output variable number of characters at each time step, which enables the model
to capture longer term dependency and improves the computational efficiency. We
demonstrate that the proposed Gram-CTC improves CTC in terms of both
performance and efficiency on the large vocabulary speech recognition task at
multiple scales of data, and that with Gram-CTC we can outperform the
state-of-the-art on a standard speech benchmark.
